Understanding clinicians’ strategies for providing gender-sensitive care: an exploration among pediatric rehabilitation health care providers
Bibliographic record
Abstract
PURPOSE: Although there is an increasing awareness of the critical role of gender within pediatric rehabilitation, little is known about the strategies that clinicians use to provide such care. The purpose of this study was to explore clinicians' strategies for providing gender-sensitive care within a pediatric rehabilitation hospital. METHODS: We used a qualitative needs assessment design and a convenience sampling strategy to recruit clinicians from a pediatric rehabilitation hospital. We conducted interviews with 23 pediatric rehabilitation health care providers from various disciplines. We applied a thematic analysis to the interview transcripts. RESULTS: Our analysis revealed the following themes regarding clinicians' strategies in providing gender-sensitive care: (1) awareness of gender biases and not making assumptions; (2) recognizing gender-based vulnerabilities; (3) respecting patient values, preferences and needs; and (4) advocacy. CONCLUSION: Health care providers working within pediatric rehabilitation have several strategies for providing a gender-sensitive care approach to clients.IMPLICATIONS FOR REHABILITATIONClinicians should seek training (i.e., appropriate terminology, creating inclusive spaces) in how to recognize gender-based health vulnerabilities, especially among patients who identify as non-binary or transgender.Clinicians should make an effort to try to be aware of their own biases and not make gender-based assumptions.Advocacy, respecting patient values, preferences and needs are important aspects of providing gender-sensitive care.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".