Validation of the measure of processes of care for use when there is no Child Development Centre
Bibliographic record
Abstract
INTRODUCTION: Clinical governance requires measurement of the outcomes of health care. The Measure of Processes of Care (MPOC; King, Rosenbaum & King 1995) is a postal questionnaire developed in Canada to reflect parents' perceptions of the quality of services received. AIM: To examine the usefulness of the MPOC for the evaluation of services for children with disabilities and their families. METHODS: The MPOC was revised minimally for British idiom, taking out explicit references to a 'Centre'. Surveys of parents using child disability services were carried out in three rural and two urban communities, where there are no Child Development Centres, with a total of 495 analysable questionnaires returned. In addition, 32 parents using a home nursing service for children with multiple disabilities completed questionnaires. RESULTS: The rate of return ranged from 49% to 67%. Factor analysis confirmed a five-factor solution but only one factor mapped clearly onto the Canadian structure. Using the revised structure, the surveys provide evidence of the discriminating potential of the MPOC, comparing the perceptions of parents who do or do not have a care coordinator, and comparing recipients of a home nursing service with matched users of general services. CONCLUSIONS: The MPOC can be used as a measure of outcome for child disability services of differing organizational structures.
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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 | 0.034 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".