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Nurses?? Views on Work Enabling Factors

2005· article· en· W2032817660 on OpenAlexaff
Marianne McLennan

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2005
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsIsland Health
Fundersnot available
KeywordsWork (physics)Job satisfactionHealth carePsychologyDimension (graph theory)Process (computing)Quality (philosophy)Applied psychologySurvey data collectionNursingKnowledge managementMedical educationComputer scienceMedicineSocial psychologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: In this study nurses' views on work enabling factors, priority issues for improvement, and stress and satisfaction levels were examined. BACKGROUND: This research builds on previous work studies that empirically established work dimension indices related to individual and organizational outcomes. A survey tool was adapted so it replicated previous instrument development research, as well as explored its practicality as a benchmark and process tool in healthcare organizations. METHODS: A descriptive design was used in a single institution employing a self-report survey. RESULTS: All of the enabling factors were found to be important to nurses. The lowest-rated factors related to manager-staff relationships, congruent with open-ended responses that identified improving management as the highest priority. Unlike previous studies, job insecurity and trust were not issues, and nurses predominantly (92%) reported that quality care was provided. CONCLUSIONS: A simple enabling index and survey tool can be used to proactively assess work environments, so that conditions can be improved before they lead to morale, sickness, and retention problems. The survey results provide important feedback that can prompt discussions about how workplaces can become healthier, more productive, and rewarding places for 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.077
GPT teacher head0.379
Teacher spread0.302 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2005
Admission routes1
Has abstractyes

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