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Creating a Healthy Workplace

2004· article· en· W1985668016 on OpenAlexafffund
Mélanie Lavoie‐Tremblay

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2004
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsGeneral partnershipCitizen journalismHealth careKnowledge managementParticipatory action researchIntervention (counseling)Work (physics)Public relationsPsychologySociologyMedicineNursingBusinessEngineeringPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Participatory research is, in theory, an approach that is attractive and easy to apply. However, in practice this type of research poses numerous challenges. This article presents recent information on healthcare workers who have experienced a participatory organizational intervention aimed at reducing the work constraints and creating a healthy workplace. Three main challenges emerged from this experience: mobilizing the team and creating a relationship of trust among the participants; creating a healthy organization in partnership with those in other professions; and implementing the proposed changes.

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)0.019
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0080.006
Open science0.0010.014
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.054
GPT teacher head0.437
Teacher spread0.383 · 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.

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

Citations31
Published2004
Admission routes2
Has abstractyes

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