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Record W2094682029 · doi:10.1177/0022185610397143

Research Note: Workplace Child Care and Elder Care Programs and Employee Retention

2011· article· en· W2094682029 on OpenAlexaffabout
Sara L. Mann, Gordon B. Cooke, Işık U. Zeytinoglu

Bibliographic record

VenueJournal of Industrial Relations · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsMcMaster UniversityMemorial University of NewfoundlandUniversity of Guelph
Fundersnot available
KeywordsEmployee retentionChild careNursingBusinessPsychologyElder carePublic relationsMedicineMarketingPolitical science

Abstract

fetched live from OpenAlex

Using Statistics Canada’s Workplace and Employee Survey (WES) data for 2003 and 2004, this research note addresses an important component of labour market retention by investigating whether the presence of workplace child care and elder care programs influences employees’ decision to quit. The key findings are as follows: (a) workplace elder care support is almost non-existent in Canada; (b) employees are more likely to remain with an organization that offers workplace child care support programs; and (c) those employees who actually use the workplace child care support are even more likely to stay with the organization. We suggest that future research should assess whether the particular support programs themselves ‘cause’ employees to stay, or whether there are other factors (within organizations offering these support programs) that account for the retention.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.848
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.158
GPT teacher head0.369
Teacher spread0.211 · 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

Citations5
Published2011
Admission routes2
Has abstractyes

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