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Record W2066053730 · doi:10.12927/cjnl.2015.24058

Nurses’ Expert Opinions of Workplace Interventions for a Healthy Working Environment: A Delphi Survey

2014· article· en· W2066053730 on OpenAlexaffvenue
Diane Doran, Sean P. Clarke, Laureen Hayes, Vera Nincic

Bibliographic record

VenueNursing leadership · 2014
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMentorshipPsychological interventionTeamworkDelphi methodNursingStaffingPsychologyDelphiMedicineMedical educationPolitical science

Abstract

fetched live from OpenAlex

Much has been written about interventions to improve the nursing work environment, yet little is known about their effectiveness. A Delphi survey of nurse experts was conducted to explore perceptions about workplace interventions in terms of feasibility and likelihood of positive impact on nurse outcomes such as job satisfaction and nurse retention. The interventions that received the highest ratings for likelihood of positive impact included: bedside handover to improve communication at shift report and promote patient-centred care; training program for nurses in dealing with violent or aggressive behaviour; development of charge nurse leadership team; training program focused on creating peer-supportive atmospheres and group cohesion; and schedule that recognizes work balance and family demands. The overall findings are consistent with the literature that highlights the importance of communication and teamwork, nurse health and safety, staffing and scheduling practices, professional development and leadership and mentorship. Nursing researchers and decision-makers should work in collaboration to implement and evaluate interventions for promoting practice environments characterized by effective communication and teamwork, professional growth and adequate support for the health and well-being of nurses.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.356
GPT teacher head0.452
Teacher spread0.096 · 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 designNot applicable
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

Citations6
Published2014
Admission routes2
Has abstractyes

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