The training needs for gender-sensitive care in a pediatric rehabilitation hospital: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Gender is an important social determinant of health; however, clinicians often lack training in how to provide gender-sensitive care. Offering appropriate and relevant training could help to address some gender-based health inequalities. Our objective was to identify and describe the training needs for gender-sensitive care among pediatric rehabilitation healthcare providers. METHODS: This study used an interpretive descriptive qualitative design to conduct interviews with 23 pediatric rehabilitation healthcare providers (19 women, 3 men, 1 transgender man), from a pediatric rehabilitation hospital in a large urban center, in Ontario, Canada from a range of disciplines. Interviews were transcribed verbatim and analyzed using an open-coding inductive thematic analysis. RESULTS: Our analysis revealed the following themes: [1] lack of knowledge about gender-sensitive care and the need for more training; [2] content of the desired training (i.e., gender differences, effective communication and how to practice gender-sensitive care) and [3] delivery method of the training. CONCLUSIONS: Enhanced gender-sensitive training for healthcare providers is required for optimizing patient outcomes and addressing gender-based health inequalities. Educators in pediatric rehabilitation should consider developing gender-sensitive care training that is embedded within post-graduate education and also continuing education within hospitals and community care centers.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".