Bibliographic record
Abstract
In this study, a Delphi Method was used to collect and collate opinions of 24 Albt-ta child care professionals regarding the creation of a research agenda on child care. Findings indicated that the 25 research questions (out of an original list of SO questions) considered important or very important by at least tnree-quarters of the participants were spread across 15 topic areas. The eight research questions considered most important were, in order of importance: (1) What knowledge, skills, and attitudes do caregivers need in order to be effective? (2) What is the impact on the quality of care if staff are trained or untrained? (3) What happens to children in family day home care and what are the child outcomes? (4) What criteria are to be used for providing good quality care for infants and toddlers? (5) What is the relationship between pay, wcrking conditions, status, and promotion opportunities and the recruitment and retention of staff? (6) What training and personal characteristics make caregivers effective in working with special needs children? (7) What is the relationship between adherence to regulations and quality care? (8) What impact does parent involvement have on the quality of care? (96 references) (RH) Reproductions supplied by EDRS are the best that can be made from the original document.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.370 | 0.123 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".