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Record W3010223372 · doi:10.1177/0844562120908747

Detriments of a Self-Sacrificing Nursing Culture on Recruitment and Retention: A Qualitative Descriptive Study

2020· article· en· W3010223372 on OpenAlexaffvenue
Sylwia D. Ciezar-Andersen, Kathryn King‐Shier

Bibliographic record

VenueCanadian Journal of Nursing Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSacrificeNursingBurnoutQualitative researchMedicinePresenteeismPsychologySocial psychologyClinical psychologyAbsenteeismSociology

Abstract

fetched live from OpenAlex

AIM: To investigate the presence and impact of self-sacrifice within the nursing profession. BACKGROUND: Evidence suggests the existence of a culture of self-sacrifice within nursing, but its potential detriments to the profession have not been explored. DESIGN: A qualitative descriptive approach was used. METHODS: Semistructured interviews were conducted with 10 practicing nurses to explore the existence and potential implications of a self-sacrificing culture within nursing. RESULTS: All participants reported self-sacrifice within the nursing profession as the result of the prevailing stereotypical image of the "ideal nurse," leading to job dissatisfaction, presenteeism, and burnout. Younger nurses reported being less willing to self-sacrifice and consequently felt unsupported by management and senior staff, resulting in job dissatisfaction and intent to leave their job. CONCLUSION: A culture of self-sacrifice within the nursing profession may lead to job dissatisfaction, presenteeism, burnout, and retention problems, especially for younger nurses. A self-sacrificing image of nursing may also deter potential recruits from exploring a career in the profession.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.627
GPT teacher head0.617
Teacher spread0.010 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
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

Citations25
Published2020
Admission routes2
Has abstractyes

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