Abstract 237: Do We Treat Women Differently? Health Care Providers’ Perspectives on Gender Differences in Post-arrest Care
Bibliographic record
Abstract
Objective: To explore the perspectives of health care providers (HCP) on previously observed gender-related differences in post-arrest care, and to identify potential barriers to care delivery amenable to modification that may minimize disparities in care. Methods: Qualitative study based on semi-structured interviews conducted with 27 HCPs at six hospitals in southern Ontario from July 2012 through March of 2013. A combination of purposeful and snowball sampling was used to recruit representative participants: gender, age, professional designation, and practice settings). Interviews were conducted by phone or in-person and lasted from 15-60 minutes. All interviews were audio-taped, transcribed, and coded inductively using a descriptive content analytic approach to identify common themes and patterns (constant comparison). Results: Most HCPs indicated that they did not feel that the care offered to female and male post-arrest patients was markedly different; rather each patient is treated as “a life to be saved” where treatment is by protocol and each arrest is context-dependent. In attempting to address potential differences in care, participants characterized gender as one of many contributing factors (clinical, social and individual circumstances), that are entangled with other social determinants of health (age, family structure, ethnicity). Factors operating at the individual-, institutional- and social structural levels may explain any observed differences. For example, once patients are transferred to the ICU for ongoing management, many HCPs felt that decision-making surrounding withdrawal of life support might differ based on gender, but intersects closely with other factors including family support, ethnicity and religion. Conclusion: HCPs do not perceive gender-based differential access to evidence-based resuscitation care is occurring. They do not believe differential access is contributing to observed differences in post-arrest outcomes between men and women. Rather, gender was portrayed as embedded within an array of social determinants of health as well as clinical and physiological factors that were difficult to separate. Future studies should focus on these other factors and explore how they relate to gender.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".