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Record W4387459381 · doi:10.1101/2023.10.06.23296671

Empowering Women in Healthcare: Unveiling Their Experiences and Strategies for Organizational Support

2023· preprint· en· W4387459381 on OpenAlexaffabout
Abi Sriharan, Nigar Sekercioglu, Whitney Berta, Sylvain Boet, Audrey Laporte, Gillian Strudwick, Senthujan Senkaiahliyan, Savithiri Ratnapalan

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsCentre for Addiction and Mental HealthUniversity of OttawaMcMaster UniversityHospital for Sick ChildrenYork UniversitySickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsWorkforceThematic analysisHealth careBurnoutNursingPsychologyOrganizational cultureScope of practiceQualitative researchJob satisfactionPublic relationsMedicineSocial psychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

ABSTRACT Importance Health care systems worldwide are grappling with rising burnout among health care workers, leading to increased rates of early retirement and job transitions. This crisis is detrimentally affecting the quality of patient care, contributing to long wait times, decreased patient satisfaction, and a heightened frequency of patient safety incidents and medical errors. Notably, women constitute 70% of the health care workforce. Objective The primary objective of this study is to uncover the factors influencing the turnover intentions and sustained commitment of HCWs who self-identify as women. Design, Setting, and Participants 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. Results In the analysis we uncovered three factors influencing women’s intent to exit the frontline workforce: organizational, professional, and personal. Organizational factors related to work related policies, compensation, positive work culture, and effective leadership behaviors emerged as essential elements for retaining women in health care organizations. Conclusions and Relevance The outcomes of this study shed light that women’s intention to leave frontline clinical roles is shaped by three interacting factors: personal, professional, and organizational. Although the personal factors are beyond the scope of organizations in retaining women in the frontline clinical care, organizations can shape organizational strategies, organizational culture and leadership approaches to ensure they are women friendly and transform the organizational environment by creating a thriving culture for women to perform their professional role in the organizations within the constraints of their personal circumstances, such as care giving responsibilities at home. Key Points Question Why do women in health care depart from frontline clinical practice, and what proactive measures can organizations implement to ensure their continued presence and contribution to patient care at the forefront? Findings In this qualitative study, involving interviews with a diverse group of health care professionals who self-identify as women, participants pinpointed three interconnected factors influencing their choices to exit clinical practice: personal circumstances, professional roles, and the organizational context. They emphasized that fostering an organizational culture that supports women, offers equitable rewards, and provides robust and supportive leadership is imperative for retaining them in frontline positions. Meaning Although personal circumstances and the inherent nature of professional roles may be beyond the direct control of organizations, they can actively shape the organizational context to create a more women-friendly environment. This reshaping entails fostering a supportive organizational culture for women, implementing fair and equitable reward systems, and providing comprehensive training for managers and leaders in talent management strategies. These concerted efforts can significantly contribute to retaining women within frontline work environments.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.010
Scholarly communication0.0080.005
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.060
GPT teacher head0.347
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainIncentives
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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