Validity, Reliability, and Relevance of a Measurement Tool for Childcare Providers’ Work-Related Stress and Job Satisfaction
Bibliographic record
Abstract
Research Findings: Childcare provider stress and job satisfaction has been found to influence childcare quality in high-income contexts, but this phenomenon has yet to be studied in a low- or middle-income country. In 2019–2020, we tested the reliability and validity of the Child Care Center Work Environment Scale (CCCWES) with 416 childcare providers in Da Nang and Quang Nam provinces, Vietnam. We assessed content and face validity and utilized item information within each factor to refine the CCCWES. We assessed convergent validity by evaluating the association between the constructs and childcare quality. In this setting, the original 50-item scale could be shortened to a reliable and valid 22-item scale. The scale comprised two factors: workplace stress and job satisfaction. Workplace stress was negatively associated with childcare quality (β=-0.100,95%CI=(−0.198,0.003)). Job satisfaction was positively associated with childcare quality (β = 0.154,95%CI=(0.056,0.251)). Practice or Policy: There is a need to measure work-related stress and job satisfaction among childcare providers in order to appropriately target interventions to reduce stress and to optimize support. We offer programs a short-form instrument that reliably measures childcare providers’ workplace stress and job satisfaction in Vietnam.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".