MétaCan
Menu
Back to cohort
Record W3161989193 · doi:10.1097/jom.0000000000002246

Workplace Discrimination and Short Sleep Among Healthcare Workers

2021· article· en· W3161989193 on OpenAlexaff
Dale Dagar Maglalang, Carina Katigbak, María Andrée López Gómez, Glorian Sorensen, Karen Hopcia, Dean M. Hashimoto, Shanta Pandey, David T. Takeuchi, Erika L. Sabbath

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsMemorial University of Newfoundland
FundersNational Institute for Occupational Safety and HealthNational Heart, Lung, and Blood Institute
KeywordsHealth careOddsAssociation (psychology)Sleep (system call)Odds ratioPsychologyOccupational safety and healthNursingMedicineLogistic regressionPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: Examine the association of discrimination and short sleep and the buffering effect of people-oriented culture in the workplace among nurses and patient care associates. METHODS: Used a mixed-methods design from the 2018 Boston Hospital Workers Health Study (N = 845) and semi-structured interviews among nurse directors (N = 16). RESULTS: We found that people-oriented culture reduced the odds of short sleep and slightly attenuated the association of discrimination and short sleep. People-oriented culture did not buffer the effects of discrimination on short sleep. Qualitative findings showed that discrimination occurred between co-workers in relation to their job titles and existing support in the workplace does not address discrimination. CONCLUSIONS: Healthcare industries need to implement specific programs and services aimed at addressing discrimination which can potentially improve health outcomes among workers.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.372
Teacher spread0.323 · 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 designObservational
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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Occupational and Environmental MedicineSame topicRacial and Ethnic Identity ResearchFrench-language works237,207