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Record W2940726749 · doi:10.1177/107937391704000102

Nurse Absenteeism and Benefit Generosity: Evidence from Canada

2017· article· en· W2940726749 on OpenAlex
Natalie Malak

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Health and Human Services Administration · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGenerosityAbsenteeismNursingMEDLINENurse AdministratorPsychologyMedicineBusinessPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

This paper uses a benefit index based on collective agreements in each Canadian province to understand the effects employee contracts have on absenteeism of full-time nurses. In particular, I estimate the impact of this benefit index on the number of hours nurses take off in a reference week. Month and year dummy variables, as well as nurses’ individual and work-related characteristics, are included in both ordinary least squares and two-part model regressions. My main finding implies full-time nurses in the most generous collective agreement show a nineteen percent increase in the number of hours taken off work in comparison to the least generous collective agreement. This study illustrates how the generosity of benefits in collective agreements varies the rate of absenteeism and highlights that employees and employers still have gains to be made from readjusting employee contracts

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
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 head0.079
GPT teacher head0.424
Teacher spread0.345 · 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