Deconstructing child care, understanding the factors impacting upon staff turnover in child care centres
Bibliographic record
Abstract
Using the 'You Bet I Care!' (1998) cross-Canada dataset, this study establishes the validity of the Marshall/Lero model of staff turnover in centre-based child care. Eighty-two percent of the variance in staff's intent to leave their centre was predicted from the staff's level of burnout, job satisfaction, and their perception of the quality of their centre. These three factors were influenced by one's level of supervisor support, co-worker relations, decision-making opportunities, perceived fairness of the reward system, wages, benefits, and position. In turn, perception of the fairness of the reward system, wages, and benefits are influenced by many organizational characteristics including the child vacancy rate at the centre, the staff turnover rate at the centre in the previous year, the amount of funding the centre receives from government sources, and the number of subsidized children at the centre. Province and auspice further influence these organizational characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".