Diagnosing and Discussing Sexual Abuse: A Scoping Review on Training Methods for Health Care Professionals
Bibliographic record
Abstract
Fiona Elizabeth van Zyl-Bonk, Sibylle Lange, Antoinette Leonarda Maria Lagro-Janssen, Theodora Alberta Maria Teunissen Department of Primary and Community Care, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the NetherlandsCorrespondence: Theodora Alberta Maria Teunissen, Department of Primary and Community Care, Research Institute for Medical Innovation, Radboud University Medical Center, Geert Grooteplein 21, Postbus 9101, Nijmegen, 6500 HB, the Netherlands, Tel +31 24 3618181, Email doreth.teunissen@radboudumc.nlPurpose: Sexual abuse is a health issue with many consequences. Recognizing and discussing past sexual abuse has proven to be challenging for health care professionals. To improve overall quality of health care for sexual abuse victims, health care professionals need to be properly trained. The aim of this paper is to provide an overview of training methods for health care professionals and to report on their effectiveness.Methods: A scoping review was conducted. A broad search was executed in six databases in December 2022. Study selection was performed by two independent reviewers, followed by quality assessment and data extraction.Results: After screening of titles and abstracts and later full-text assessment for quality appraisal, seven articles were selected, consisting mostly of non-randomized trials, performed among a total of 1299 health care professionals. All studies were assessed to be of moderate to poor quality. The participants attended training courses with a wide variety of durations, settings, formats and methods. The outcomes showed improvements in self-perceived or measured knowledge, skills and confidence to discuss sexual violence. Changes in clinical practice were scarcely investigated. Training courses were most effective when a mix of didactic passive methods, such as lectures and videos, and active participatory strategies, such as discussions and roleplay, were applied. Timely iteration to reinforce retention of gained knowledge and skills also contributed to effectiveness. Participants most enjoyed incorporating opportunities for receiving feedback in small settings and sharing personal experiences.Conclusion: This scoping review summarizes on how to effectively train health care professionals. Flaws and difficulties in measuring the effectiveness of training courses were discussed. Recognition and discussion of past sexual abuse by health care providers can be effectively trained using an alternating mix of multiple active and passive training methods with room for feedback and personal experiences.Keywords: sexual violence, disclosure, recognition, medical education, post-graduate training
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".