Addressing the health human resources crisis: Strategies for retaining women health care professionals in organizations
Bibliographic record
Abstract
Globally, healthcare systems are contending with a pronounced health human resource crisis marked by elevated rates of burnout, heightened job transitions, and an escalating demand for the limited supply of the existing health workforce. This crisis detrimentally affects the quality of patient care, contributing to long wait times, decreased patient satisfaction, and a heightened frequency of patient safety incidents and medical errors. In response to the heightened demand, healthcare organizations are proactively exploring solutions to retain their workforce. With women comprising over 70% of health human resources, this study seeks to gain insight into the unique experiences of women health professionals on the frontlines of healthcare and develop a conceptual framework aimed at facilitating organizations in effectively supporting the retention and advancement of women in healthcare frontline roles. We used grounded theory in this qualitative study. From January 2023 to May 2023, we conducted individual semi-structured interviews with 27 frontline HCWs working in Canada and representing diverse backgrounds. The data underwent thematic analysis, which involved identifying and comprehending recurring patterns across the information to elucidate emerging themes. Our analysis found that organizational, professional, and personal factors shape women's intentions to leave the frontline workforce. Reevaluating organizational strategies related to workforce, fostering a positive work culture, and building the capacity of management to create supportive work environment can collectively transform the work environment. By creating conditions that enable women to perform effectively and find satisfaction in their professional roles, organizations can enhance their ability to retain valuable talent.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 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".