Outcomes of gender-sensitivity educational interventions for healthcare providers: A systematic review
Bibliographic record
Abstract
Background: Although gender plays an important role in health, most healthcare providers lack knowledge in providing gender-sensitive care. Offering gender-sensitivity training for healthcare providers can help to address gender-based health inequalities. Method: A mixed-methods systematic review of gender-sensitivity training programmes or interventions for healthcare providers was undertaken to assess their outcomes and to document areas for future research. Comprehensive searches of seven international databases were conducted for peer-reviewed literature published between 1998 and 2018. Eligible studies included at least one outcome related to gender-sensitivity training for healthcare providers. Results: Twenty-nine studies met the inclusion criteria. Fourteen studies focused on gender-sensitivity in reducing gender bias towards men and women, and 15 studies focused on addressing the needs of lesbian, gay, bisexual and transgender (LGBT) patients. Thirty-seven percent of studies showed a significant improvement in gender-related knowledge, attitudes or practice after the training. Multiple training methods were used to teach gender-sensitive care. Common content of the training included learning sex/gender terminology, understanding gender issues and inequalities in health, stigma and discrimination and communication skills. The duration and frequency of interventions ranged considerably. Sex differences in training outcomes also occurred among the learners. Conclusion: Review findings highlight that although gender-sensitivity training for healthcare providers is increasing, there is insufficient evidence to determine its effectiveness. Additional, more rigorously designed studies are needed to assess the long-term implications on learner behaviours and practices, especially across a wide variety of healthcare providers.
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".