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Record W4386255702 · doi:10.3390/ijerph20176664

“Collapsing into Darkness”: An Exploratory Qualitative Thematic Analysis of the Experience of Workplace Reintegration among Nurses with Operational Stress Injuries

2023· article· en· W4386255702 on OpenAlexafffundabout
Chelsea Jones, Brenda Juby, Shaylee Spencer, Lorraine Smith‐MacDonald, Elly O’Greysik, Michelle Vincent, Colleen Mooney, Katherine Bright, Phillip R. Sevigny, Lisa Burback, Andrew J. Greenshaw, R. Nicholas Carleton, Raymond S. Savage, Jake Hayward, Yanbo Zhang, Bo Cao, Suzette Brémault‐Phillips

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsRoyal Canadian Mounted PoliceCanadian Institute for Public Safety Research and TreatmentMount Royal UniversityUniversity of ReginaUniversity of CalgaryAlberta Health ServicesThe King's UniversityMacEwan UniversityMontreal Police ServiceUniversity of Alberta
FundersAlberta Health Services
KeywordsThematic analysisNursingQualitative researchStatus quoFocus groupPsychological interventionPsychologyOccupational safety and healthExploratory researchMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.500

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.078
GPT teacher head0.454
Teacher spread0.376 · 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 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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

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