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Record W1698689357 · doi:10.3233/wor-2010-1077

Social protection and the employment contract: The impact on work absence

2010· article· en· W1698689357 on OpenAlexaffabout
Emile Tompa, Heather Scott‐Marshall, Miao Fang

Bibliographic record

VenueWork · 2010
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsInstitute for Work & HealthMcMaster UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsDemographic economicsDuration (music)Temporary workEmployment protection legislationHazardWork (physics)Longitudinal dataSample (material)Employment contractLongitudinal samplePsychologyLabour economicsBusinessEconomicsDemographyUnemploymentSociologyEconomic growthDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study investigates the impact of temporary employment on all-cause sickness absence of one week or more with a focus on how this relationship is moderated by factors related to social protection (job tenure, union membership and firm size). PARTICIPANTS: A sample of 5,307 individuals who experienced 9,574 distinct job episodes was drawn from a longitudinal Canadian labour market survey (2000-2004). METHODS: Duration analysis was undertaken to model the time from the start of a job to the first sickness absence. Specifically, a proportional hazard model was estimated using a complementary log-log function for continuous time processes. RESULTS: Findings showed that temporary employment was associated with a lower rate of sickness absence after controlling for tenure, prior health status, and several other individual and job characteristics. CONCLUSIONS: The results suggest that the lack of social protection in temporary jobs is a powerful determinant of absence taking, even in the case of serious health conditions that require an absence of one week or more.

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)0.001
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 score0.296
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.045
GPT teacher head0.398
Teacher spread0.353 · 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.

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

Citations16
Published2010
Admission routes2
Has abstractyes

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