Additional Supervision Research Articles

A Supervision Framework for Task-Shared Mental Health Workers: Implications for Clinical Trials and Beyond.

The rise in task-shared interventions that address the mental health treatment gap in low- and middle-income countries (LMICs) has highlighted the need for additional support and supervision of nonspecialist mental health workers (NHWs). The supervision of NHWs in most resource-limited settings is still primarily disorganized, without clear guidelines that provide the necessary structure for supervision. The need for supervision is even greater for NHWs working in the context of trauma, not only to provide training and ensure adequate delivery of care but also to provide support to minimize the psychological impact of their work. In South Africa, women face intersecting epidemics of HIV, intimate partner violence, and sexual trauma. This syndemic highlights the importance of integrating mental health treatment in HIV care, especially in settings like South Africa, where mental health services are limited. In this context, our group developed and is evaluating the effectiveness of ImpACT+, a task-shared coping intervention to improve clinical and mental health outcomes among HIV-infected women with sexual trauma in South Africa. We describe the ImpACT+ supervision model that is currently being implemented in the context of a hybrid effectiveness-implementation clinical trial. Combining experiences from clinical psychology, task-shared interventions, and trauma-informed care, the supervision model integrates formal elements of clinical supervision into categories that are suitable for use in task-shared trauma interventions in low-resource settings. To the best of our knowledge, such a trauma-informed supervision approach has not been widely documented in the literature, particularly in task-shared interventions in LMICs. In this article, we describe the ImpACT+ intervention, provide an overview of the supervision model, and provide illustrative examples of how the supervision model has been implemented.

A scoping review of the post-discharge care needs of babies requiring surgery in the first year of life.

Congenital anomalies are among the leading causes of under-5 mortality, predominantly impacting low- and middle-income countries (LMICs). A particularly vulnerable group are babies with congenital disorders requiring surgery in their first year. Addressing this is crucial to meet SDG-3, necessitating targeted efforts. Post-discharge, these infants have various care needs provided by caregivers, yet literature on these needs is scant. Our scoping review aimed to identify the complex care needs of babies post-surgery for critical congenital cardiac conditions and non-cardiac conditions. Employing the Joanna Briggs Institute's methodological framework for scoping reviews we searched Pubmed, EMBASE, CINAHL, PsychINFO, and Web of Science databases. Search terms included i) specific congenital conditions (informed by the literature and surgeons in the field), ii) post-discharge care, and iii) newborns/infants. English papers published between 2002-2022 were included. Findings were summarised using a narrative synthesis. Searches yielded a total of 10,278 papers, with 40 meeting inclusion criteria. 80% of studies were conducted in High-Income Countries (HICs). Complex care needs were shared between cardiac and non-cardiac congenital conditions. Major themes identified included 1. Monitoring, 2. Feeding, and 3. Specific care needs. Sub-themes included monitoring (oxygen, weight, oral intake), additional supervision, general feeding, assistive feeding, condition-specific practices e.g., stoma care, and general care. The post-discharge period poses a challenge for caregivers of babies requiring surgery within the first year of life. This is particularly the case for caregivers in LMICs where access to surgical care is challenging and imposes a financial burden. Parents need to be prepared to manage feeding, monitoring, and specific care needs for their infants before hospital discharge and require subsequent support in the community. Despite the burden of congenital anomalies occurring in LMICs, most of the literature is HIC-based. More research of this nature is essential to guide families caring for their infants post-surgical care.

Feature mixing and self-training for source-free active domain adaptation

Recently, active domain adaptation (ADA) has become an emerging paradigm, as it significantly boosts the performance of the model with additional supervision. Despite the impressive performance of ADA methods, they are assumed that the source domain can be available during model adaptation. In many scenarios, the source domain can be unavailable due to issues such as data privacy. Consequently, ADA methods fail to achieve the expected results or even become invalid. To solve the problem, we design an effective framework named Feature Mixing and Self-Training (FMAS) for source-free active domain adaptation. Specifically, we adopt Clustering-based Alignment to make the classification boundary clearer to be beneficial to the subsequent sample selection, which can also achieve initial domain adaptation. When reaching active sample selection, we utilize feature mixing to explore the uncertainty and diversity of the sample. We construct the sample candidate pool by Mixup-based Uncertainty Selection and then consider the diversity to overcome the redundancy among the pool by Entropy-based Diversity Selection. Finally, for the remaining unlabeled samples, we adopt the self-training framework, which screens out high-confidence samples to explore the information of unlabeled samples. Thorough experiments show that FMAS improves average accuracy by at least 1% compared to other methods on benchmark datasets OfficeHome and MiniDomainNet. And FMAS can also achieve better results with ADA methods without the source domain data, which demonstrates FMAS can outperform previous ADA methods.

A pseudo-labeling based weakly supervised segmentation method for few-shot texture images

Automatic segmentation of key region shapes from material microstructure images is one of the primary steps to count material phase data and mine material properties. With the rapid development of deep learning networks, the image segmentation task has achieved great success. However, deep learning-based material microstructure image segmentation still faces the problems of sample scarcity, annotation difficulty and model generalizability. These problems lead to the poor performance of existing deep learning networks on material microstructure image segmentation. To address these problems, we propose a weakly supervised pseudo-labeling texture semantic segmentation (PTS) network based on scribble annotations. The PTS network uses the auxiliary branch to generate pseudo labels for additional supervision and to optimize the scribble annotation segmentation results. This achieves an improvement in segmentation accuracy and generalization ability. We conduct experiments for the PTS network on small-sample titanium alloy images, ceramic images and carbon steel images. The segmentation results of the PTS network achieves 14.4%∼19.8% improvement in mIoU (mean intersection over Union) and 9.5%∼18% improvement in mDice (mean Dice) compared to the existing deep learning networks. Extensive experiments have validated that the PTS network can well overcome the problems of sample scarcity, annotation difficulty and model generalizability in material microstructure image segmentation.

Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume.

Age-related Macular Degeneration (AMD) is a degenerative eye disease that causes central vision loss. Optical Coherence Tomography Angiography (OCTA) is an emerging imaging modality that aids in the diagnosis of AMD by displaying the pathogenic vessels in the subretinal space. In this paper, we investigate the effectiveness of OCTA from the view of deep classifiers. To the best of our knowledge, this is the first study that solely uses OCTA for AMD stage grading. By developing a 2D classifier based on OCTA projections, we identify that segmentation errors in retinal layers significantly affect the accuracy of classification. To address this issue, we propose analyzing 3D OCTA volumes directly using a 2D convolutional neural network trained with additional projection supervision. Our experimental results show that we achieve over 80% accuracy on a four-stage grading task on both error-free and error-prone test sets, which is significantly higher than 60%, the accuracy of human experts. This demonstrates that OCTA provides sufficient information for AMD stage grading and the proposed 3D volume analyzer is more robust when dealing with OCTA data with segmentation errors.

Relational Query Synthesis ⋈ Decision Tree Learning

We study the problem of synthesizing a core fragment of relational queries called select-project-join (SPJ) queries from input-output examples. Search-based synthesis techniques are suited to synthesizing projections and joins by navigating the network of relational tables but require additional supervision for synthesizing comparison predicates. On the other hand, decision tree learning techniques are suited to synthesizing comparison predicates when the input database can be summarized as a single labelled relational table. In this paper, we adapt and interleave methods from the domains of relational query synthesis and decision tree learning, and present an end-to-end framework for synthesizing relational queries with categorical and numerical comparison predicates. Our technique guarantees the completeness of the synthesis procedure and strongly encourages minimality of the synthesized program. We present Libra, an implementation of this technique and evaluate it on a benchmark suite of 1,475 instances of queries over 159 databases with multiple tables. Libra solves 1,361 of these instances in an average of 59 seconds per instance. It outperforms state-of-the-art program synthesis tools Scythe and PatSQL in terms of both the running time and the quality of the synthesized programs.

Weakly supervised joint whole-slide segmentation and classification in prostate cancer.

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which are tedious and expensive to collect for whole-slide images (WSI). Though several methods have been developed to exploit image-level weak-supervision for WSI classification, the task of segmentation using WSI-level labels has received very little attention. The research in this direction typically require additional supervision beyond image labels, which are difficult to obtain in real-world practice. In this study, we propose WholeSIGHT, a weakly-supervised method that can simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WholeSIGHT first constructs a tissue-graph representation of WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo-labels via post-hoc feature attribution. These pseudo-labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render segmentation and class prediction for an input WSI. We evaluate the performance of WholeSIGHT on three public prostate cancer WSI datasets. Our method achieves state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we assess the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. Our code is available at: https://github.com/histocartography/wholesight.

Fast and robust for texture-less feature registration via adaptive heterogeneous kernels

Feature registration is a core problem in computer vision and machine learning techniques. In recent years, significant progress has been made in learning-based feature registration algorithms. However, such methods usually capture image-level features and strive to capture contour information, inadvertently neglecting the exploration of low-texture regions, while the complex model structure brings more parameters. Therefore, it remains a challenging task to effectively address low-texture feature registration under limited sample conditions. In this paper, we propose a fast and robust feature registration algorithm to achieve fast feature registration in the low-texture case. Specifically, we first construct a two-step learning framework and use a faster adaptive heterogeneous filter (AdaH-Filter) to guide coarse-grained and fine-grained feature extraction. Then, we propose registration feature refiners (ReFR) to achieve the fusion of two-stage features in a low-texture environment while guaranteeing the speed of the detector. Finally, to further improve the network performance, a Warp Extractor is proposed to improve the speed and portability of model inference. This strategy allows the network to perform different levels of fine extraction adaptively according to the registration requirements. Moreover, the proposed method is trained in an end-to-end manner, guided by cosine loss, without any additional supervision. Extensive experimental results on several challenging and representative feature registration datasets in the industry show that the proposed method outperforms the existing state-of-the-art methods. More importantly, it has a faster inference speed under different sensor modalities.

Improving segmentation and detection of lesions in CT scans using intensity distribution supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% → 47.8%, 74.2% → 76.0%, and 26.4% → 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% → 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.

2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery

Visual navigation based on image matching has become one of the most important research fields for UAVs to achieve autonomous navigation, because of its low cost, strong anti-jamming ability, and high performance. Currently, numerous positioning and navigation methods based on visual information have been proposed for UAV navigation. However, the appearance, shape, color, and texture of objects can change significantly due to different lighting conditions, shadows, and surface coverage during different seasons, such as vegetation cover in summer or ice and snow cover in winter. These changes pose greater challenges for feature-based image matching methods. This encouraged us to overcome the limitations of previous works, which did not consider significant seasonal changes such as snow-covered UAV aerial images, by proposing an image matching method using season-changing UAV aerial images and satellite imagery. Following the pipeline of a two-channel deep convolutional neural network, we first pre-scaled the UAV aerial images, ensuring that the UAV aerial images and satellite imagery had the same ground sampling distance. Then, we introduced attention mechanisms to provide additional supervision for both low-level local features and high-level global features, resulting in a new season-specific feature representation. The similarity between image patches was calculated using a similarity measurement layer composed of two fully connected layers. Subsequently, we conducted template matching to estimate the UAV matching position with the highest similarity. Finally, we validated our proposed method on both synthetic and real UAV aerial image datasets, and conducted direct comparisons with previous popular works. The experimental results demonstrated that our method achieved the highest matching accuracy on multi-temporal and multi-season images.

Assessing Confidence Levels in Endodontic Procedures Among Senior Undergraduate Dental Students at Qassim University, Saudi Arabia: A Cross-Sectional Observational Study.

Background This study aimed to determine the confidence of senior dental students in Qassim University's undergraduate dentistry program in Saudi Arabia regarding their capacity to perform endodontic procedures. The study's objective was to pinpoint areas of weakness and offer suggestions for potential improvement of endodontic and clinical training for dental students. Methodology Using anonymous survey forms, 120 senior dental students were surveyed. Students' self-reported levels of confidence in performing various endodontic procedures, such as periapical radiographs, rubber dam placement, endodontically treated tooth restoration, anesthesia, broken instrument removal, and endodontic retreatment, were studied in this survey. A Likert-style rating scale of 1 to 5 was used in the survey, with 1 denoting high confidence and 5 denoting low confidence. Results The majority of senior dental students at Qassim University reported high levels of confidence in taking periapical radiographs, with 64.04% indicating a high level of confidence. The second most assured field was rubber dam installation, with 47.6% of students expressing high levels of assurance. Confidence levels were noticeably lower during more difficult procedures such as endodontic retreatment and the removal of broken instruments. While 12% of students expressed high confidence in endodontic retreatment, only 3.46% of students expressed high confidence in removing broken instruments. According to Pearson's correlation (r = 0.352, p = 0.001), there was a significant and favorable correlation between competency level and confidence in performing various endodontic procedure-related steps. Conclusions The study's findings suggest that senior dentistry students at Qassim University might benefit from additional training and assistance in some endodontic treatment areas. The lack of confidence displayed during more challenging procedures, such as the removal of broken instruments and endodontic retreatment, demonstrates the need for additional training and supervision in such cases. The results of this study may be useful for educators in other dental colleges who wish to consider developing new teaching techniques, utilizing simulators and digital learning tools, and increasing opportunities for hands-on training and supervision to improve dental students' self-confidence and skills.

Learning Good Features to Transfer Across Tasks and Domains

Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to solve different tasks share the same architecture suggests that there should be a way of reusing the knowledge learned in a specific setting to solve novel tasks with limited or no additional supervision. In this work, we first show that such knowledge can be shared across tasks by learning a mapping between task-specific deep features in a given domain. Then, we show that this mapping function, implemented by a neural network, is able to generalize to novel unseen domains. Besides, we propose a set of strategies to constrain the learned feature spaces, to ease learning and increase the generalization capability of the mapping network, thereby considerably improving the final performance of our framework. Our proposal obtains compelling results in challenging synthetic-to-real adaptation scenarios by transferring knowledge between monocular depth estimation and semantic segmentation tasks.

Hierarchical attention aggregation with multi-resolution feature learning for GAN-based underwater image enhancement

In recent years, underwater image enhancement and restoration technologies have become increasingly important in order to optimize the efficiency of maritime operations and promote the automatic machine learning of the maritime industry. A new hierarchical attention aggregation with multi-resolution feature learning for GAN-based underwater image enhancement is proposed to address the problems of color bias, underexposure, and blurring in underwater images. The proposed method consists of a generator and a discriminator. Specifically, the generator includes an encoder, a bottleneck layer, and a decoder. Generator introduces inter-block serial connections for better adaptation to complex image scenes and task requirements, and parallel connections to extract multi-level features and enhance the expressive capacity of the network. To extract semantic and contextual information, hierarchical attention dense aggregation is designed in the encoder, which includes multi-scale feature hierarchy and dense feature hierarchy. Additionally, a multi-scale spatial attention mechanism is designed in the bottleneck layer to handle variations in underwater image scenes. In the decoder, the feature channel layer is emphasized, and a multi-channel attention mechanism is proposed to restore the multi-resolution channel features to a three-channel enhanced image. Furthermore, the angular loss function is introduced as additional supervision, which improves the similarity between the generated and original images, increases image clarity, and reduces color bias. Meanwhile, we employ the patch discriminator to enhance machine vision. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods.

A Two-Path Multibehavior Model of User Interaction

Personalized recommendation is an important part of e-commerce platforms. In recommendation systems, a neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focus on the results of a single user behavior, ignoring the modeling of multiple interaction behaviors of users, such as click, add to cart and purchase. Although many studies have also focused on multibehavior modeling, two important challenges remain: (1) Since the multiple behaviors of the time-evolving trends of context information are ignored, it is still a challenge to identify the multimodal relationships of behaviors; (2) surveillance signals are still sparse. In order to solve these problem, this paper proposes a two-path multibehavior model of user interaction (TP_MB). First, a two-path learning strategy is introduced to maximize the multiple-interaction information of users and items learned by the two paths, which effectively enhances the robustness of the model. Second, a multibehavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different users. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multibehavior recommendation methods.

Underwater Ranker: Learn Which Is Better and How to Be Better

In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. The URanker is built on the efficient conv-attentional image Transformer. In terms of underwater images, we specially devise (1) the histogram prior that embeds the color distribution of an underwater image as histogram token to attend global degradation and (2) the dynamic cross-scale correspondence to model local degradation. The final prediction depends on the class tokens from different scales, which comprehensively considers multi-scale dependencies. With the margin ranking loss, our URanker can accurately rank the order of underwater images of the same scene enhanced by different underwater image enhancement (UIE) algorithms according to their visual quality. To achieve that, we also contribute a dataset, URankerSet, containing sufficient results enhanced by different UIE algorithms and the corresponding perceptual rankings, to train our URanker. Apart from the good performance of URanker, we found that a simple U-shape UIE network can obtain promising performance when it is coupled with our pre-trained URanker as additional supervision. In addition, we also propose a normalization tail that can significantly improve the performance of UIE networks. Extensive experiments demonstrate the state-of-the-art performance of our method. The key designs of our method are discussed. Our code and dataset are available at https://li-chongyi.github.io/URanker_files/.

Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images (Student Abstract)

The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the latent space of a Deep Generative Model as a solution to these bottlenecks. To this end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative Autoencoder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images. We present qualitative and quantitative results for the same and their analysis. This work shall serve as a guiding primer for future work.

Self-Emphasizing Network for Continuous Sign Language Recognition

Hand and face play an important role in expressing sign language. Their features are usually especially leveraged to improve system performance. However, to effectively extract visual representations and capture trajectories for hands and face, previous methods always come at high computations with increased training complexity. They usually employ extra heavy pose-estimation networks to locate human body keypoints or rely on additional pre-extracted heatmaps for supervision. To relieve this problem, we propose a self-emphasizing network (SEN) to emphasize informative spatial regions in a self-motivated way, with few extra computations and without additional expensive supervision. Specifically, SEN first employs a lightweight subnetwork to incorporate local spatial-temporal features to identify informative regions, and then dynamically augment original features via attention maps. It's also observed that not all frames contribute equally to recognition. We present a temporal self-emphasizing module to adaptively emphasize those discriminative frames and suppress redundant ones. A comprehensive comparison with previous methods equipped with hand and face features demonstrates the superiority of our method, even though they always require huge computations and rely on expensive extra supervision. Remarkably, with few extra computations, SEN achieves new state-of-the-art accuracy on four large-scale datasets, PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. Visualizations verify the effects of SEN on emphasizing informative spatial and temporal features. Code is available at https://github.com/hulianyuyy/SEN_CSLR

DOVE: Learning Deformable 3D Objects by Watching Videos

Learning deformable 3D objects from 2D images is often an ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects “in the wild”. A more natural way of establishing correspondences is by watching videos of objects moving around. In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision. By resolving symmetry-induced pose ambiguities and leveraging temporal correspondences in videos, the model automatically learns to factor out 3D shape, articulated pose and texture from each individual RGB frame, and is ready for single-image inference at test time. In the experiments, we show that existing methods fail to learn sensible 3D shapes without additional keypoint or template supervision, whereas our method produces temporally consistent 3D models, which can be animated and rendered from arbitrary viewpoints. Project page: https://dove3d.github.io/.

Vaccine wastage in Ghana, Mozambique, and Pakistan: An assessment of wastage rates for four vaccines and the context, causes, drivers, and knowledge, attitudes and practices for vaccine wastage

Vaccine procurement costs comprise a significant share of immunization program costs in low- and middle-income countries, yet not all procured vaccines are administered. Vaccine wastage occurs due to vial breakage, excessive heat or freezing, expiration, or when not all doses in a multidose vial are used. Better estimates of vaccine wastage rates and their causes could support improved management of vaccine stocks and reduce procurement costs. This study examined aspects of wastage for four vaccines at service delivery points in Ghana (n = 48), Mozambique (n = 36), and Pakistan (n = 46). We used prospective data from daily and monthly vaccine usage data entry forms, along with cross-sectional surveys, and in-depth interviews. The analysis found that estimated monthly proportional open-vial wastage rates for vaccines in single-dose vials (SDV) or in multi-dose vials (MDV) that can be kept refrigerated up to four weeks after opening ranged from 0.08 % to 3 %. For MDV where remaining doses are discarded within six hours after opening, the mean wastage rates ranged from 5 % to 33 %, with rates being highest for measles containing vaccine. Despite national-level guidance to open a vaccine vial even when only one child is present, vaccines in MDV that are discarded within six hours of opening are sometimes offered less frequently than vaccines in SDV or in MDV where remaining doses can be used for up to 4 weeks. This practice can lead to missed opportunities for vaccination. While closed-vial wastage at service delivery points (SDPs) was relatively rare, individual instances can result in large losses, suggesting that monitoring closed-vial wastage should not be neglected. Health workers reported insufficient knowledge of vaccine wastage tracking and reporting methods. Improving reporting forms would facilitate more accurate reporting of all causes of wastage, as would additional training and supportive supervision. Globally, decreasing doses per vial could reduce open-vial wastage.

Fidelity and Clinical Competence in Providing Illness Management and Recovery: An Explorative Study

Illness Management and Recovery (IMR) is a psychosocial intervention supporting people with serious mental illnesses. In this study, 15 IMR groups were assessed for fidelity and clinician competency to establish the implementation level of all IMR elements and explore complementarity of the IMR Treatment Integrity Scale (IT-IS) to the standard IMR Fidelity Scale. Use of the IT-IS was adapted, similar to the IMR Fidelity Scale. Descriptive statistics were applied. Implementation success of IMR elements varied widely on the IMR Fidelity Scale and IT-IS (M = 3.94, SD = 1.13, and M = 3.29, SD = 1.05, respectively). Twelve IMR elements (60%) were well-implemented, whereas eight (40%) were implemented insufficiently, including some critical cognitive-behavioral techniques (e.g., role-playing). The scales appeared largely complementary, though strongly correlated (r (13) = 0.74, p = 0.002). Providing all IMR elements adequately requires a variety of clinical skills. Specific additional training and supervision may be necessary.