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Record W2438587723 · doi:10.1037/a0040107

Does social support buffer the effects of occupational stress on sleep quality among paramedics? A daily diary study.

2016· article· en· W2438587723 on OpenAlexafffund
Jessie Pow, David B. King, Ellen Stephenson, Anita DeLongis

Bibliographic record

VenueJournal of Occupational Health Psychology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaUniversity of British Columbia
KeywordsOccupational stressPsycINFOPsychologySocial supportMultilevel modelSleep (system call)Stress (linguistics)Sleep qualityClinical psychologyMEDLINECognitionPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Given evidence suggesting a detrimental effect of occupational stress on sleep, it is important to identify protective factors that may ameliorate this effect. We followed 87 paramedics upon waking and after work over 1 week using a daily diary methodology. Multilevel modeling was used to examine whether the detrimental effects of daily occupational stress on sleep quality were buffered by perceived social support availability. Paramedics who reported more support availability tended to report better quality sleep over the week. Additionally, perceived support availability buffered postworkday sleep from average occupational stress and days of especially high occupational stress. Perceived support availability also buffered off-workday sleep from the cumulative amount of occupational stress experienced over the previous workweek. Those with low levels of support displayed poor sleep quality in the face of high occupational stress; those high in support did not show significant effects of occupational stress on sleep. (PsycINFO Database Record

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.046
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.520
Teacher spread0.448 · 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 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

Citations82
Published2016
Admission routes2
Has abstractyes

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