Challenges with providing gender-sensitive care: exploring experiences within pediatric rehabilitation hospital
Bibliographic record
Abstract
Purpose: The purpose of this study was to explore the perceived challenges with providing a gender-sensitive care approach among pediatric rehabilitation care providers.Methods: Using a qualitative needs assessment design and a purposive sampling strategy, we recruited clinicians from a Canadian pediatric rehabilitation hospital. We conducted interviews with 23 pediatric rehabilitation healthcare providers (19 women, 3 men, 1 transgender man) from a range of disciplines. Three coders performed a thematic analysis of the transcripts.Results: Our analysis revealed the following themes regarding the perceived challenges in providing a gender-sensitive care approach: (1) a lack of training and experience; (2) gender differences and stereotypes; (3) binary documentation and potential for misgendering; (4) the complexity of gender identity; and (5) the gender of the clinician.Conclusions: Pediatric rehabilitation care providers face many challenges in offering a gender-sensitive care approach and need further training and systemic support.Implications for rehabilitationAwareness of the challenges in providing gender-sensitive care could be an important first step in helping to address inequities.Systemic and interpersonal barriers may impede the provision of gender-sensitive care among rehabilitation providers.Clinicians need more training and support in how to provide gender-sensitive care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".