Development and Evaluation of an Open-Source, Online Training for the Measurement of Adult-Child Responsivity at Home and in Early Childhood Education and Care Settings
Bibliographic record
Abstract
Efforts to monitor and improve responsive caregiving for young children, because of its importance for child development, are part of the United Nations Sustainable Development Goals. Two brief observational measures of responsive caregiving have been developed and validated (Responsive Interactions for Learning—parent [RIFL-P] and educator [RIFL-Ed] versions), with the RIFL-P available in English, Portuguese, and Spanish. The aim of the current study was to present and evaluate two online training programs for the RIFL measures. These distance learning courses were designed as open-source and asynchronous to enable their use in low- and middle-income countries and remote areas. The following course components are used: readings, lectures, observation of interactions on video, coding practice with automated feedback on item coding, and quizzes. Of the 76 trainees who registered for one of the online courses, 58 (76%) completed all theoretical module components. Student performance was generally high. Marks on quizzes ranged between 83%–100%. Ninety percent of those who took the reliability tests passed (40/44). Student satisfaction during and after the course was high. The effective online training programs are available free of charge and the RIFL suite of measures is efficient to implement. Implications for research and practice are discussed.
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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".