A Qualitative Description of Resident Physicians’ Understanding of Child Maltreatment: Impacts, Recognition, and Response
Bibliographic record
Abstract
Child maltreatment (CM) is a public health problem with devastating effects on individuals, families, and communities. Resident physicians have varied formal education in CM, and report feeling inadequately trained in identifying and responding to CM. The purpose of this study is to explore residents' understanding of the impacts of CM, and their perceptions of their role in recognizing and responding to CM to better understand their educational needs. This study analyzed qualitative data obtained from a larger project on family violence education. Twenty-nine resident physicians enrolled in pediatric, family medicine, emergency medicine, obstetrics and gynecology, and psychiatry training programs in Alberta, Ontario, and Québec participated in semi-structured interviews to elicit their ideas, experiences, and educational needs relating to CM. Conventional (inductive) content analysis guided the development of codes and categories. Residents had thorough knowledge about the impacts of CM and their duty to recognize CM, but there was less consistency in how residents understood their role in responding to CM. Residents identified the need for more education about recognizing and responding to CM, and the need for educational content to be responsive to training, patient and family factors, and systemic issues. Despite knowledge about the impacts of CM and laws pertaining to mandated reporting, residents reported challenges with responding to concerns of CM. Findings of this study emphasize the need for better training in response to CM. Future educational interventions should consider a multidisciplinary, experiential approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".