Physician Perspectives on the Implementation of a Trauma Informed Care Initiative in the Maternity Care Setting
Bibliographic record
Abstract
Objectives: To explore the barriers and facilitators from the perspective of family physicians on the implementation of a pilot trauma-informed care (TIC) initiative to promote resilience, with particular emphasis on asking about adverse childhood experiences (ACEs), in a maternity care clinic.Methods: Using an exploratory qualitative design, in-depth semi-structured interviews were conducted with family physicians who were practicing in a maternity clinic in a large Canadian city. Interviews were audio-recorded and transcribed verbatim. Transcripts were reviewed by three coders and themes were extracted using thematic analysis.Results: The analysis of 10 interviews yielded six thematic domains. Three domains pertained to perceived barriers to obtaining an ACEs history including: (1) concern about time management, (2) initial lack of physician comfort with TIC, and (3) cultural limitations of using the ACEs questionnaire. Three themes pertained to perceived facilitators of obtaining an ACEs history including: (1) the importance of a physician champion, (2) a supportive and flexible clinic environment, and (3) improved patient-physician relationships.Implications: In the context of a broader TIC initiative within a maternity care setting, asking patients about ACEs was generally perceived positively by physicians. Ensuring a supportive clinic environment and adequate staff training may be critical factors that contribute to successful implementation. Future research focused on diverse physician experiences in different settings are needed.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".