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Record W2908507294 · doi:10.1186/s13104-018-4039-5

Factors associated with the health status of childcare workers in southern Alberta, Canada

2019· article· en· W2908507294 on OpenAlexafffundabout
Olu Awosoga, Afeez Abiola Hazzan, Suzanne McIntosh, Julia Dabravolskaj, Tolulope T. Sajobi, Jon B. Doan

Bibliographic record

VenueBMC Research Notes · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsUniversity of CalgaryUniversity of Lethbridge
FundersUniversity of Lethbridge
KeywordsEnvironmental healthMedicineGerontologyDemographySociology

Abstract

fetched live from OpenAlex

OBJECTIVE: There is growing evidence that the well-being of childcare workers has important implications for the care provided to children attending childcare centers. To add to the growing body of research in this area and to lay the groundwork for further research, we report the results of a pilot study examining factors that are associated with the health status of childcare workers in southern Alberta, Canada. The factors examined include: health control, employer's interest in the childcare worker's wellbeing, and actions that childcare workers are taking to improve their own health. RESULTS: A total of 260 "Workplace Health and Risks Survey 2008" questionnaires were sent to 13 licensed daycare centers in southern Alberta, Canada. Of these, a total of 110 questionnaires were completed by childcare workers at these centers and returned. Regression analysis results show that control over one's health (Standardized Beta = .504, p < .001), employers' knowledge of negative effects of stress (Standardized Beta = - .328, p = .017), employers' interest in employees' well-being (Standardized Beta = .366, p = .008), and actions that are planned to be taken to improve or maintain health in the future (Standardized Beta = .231, p = .005) are all significant predictors of health status among childcare 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 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)0.002
metaresearch head score (Gemma)0.004
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 score0.050
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.198
GPT teacher head0.462
Teacher spread0.263 · 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

Citations7
Published2019
Admission routes3
Has abstractyes

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