Measure of Processes of Care: a review of 20 years of research
Bibliographic record
Abstract
AIM: This article reviews literature on findings from the Measure of Processes of Care (MPOC) to assess family-centred services. METHOD: Systematic searches for papers citing MPOC in both PubMed and Web of Science identified 107 articles. Fifty-five met the criterion for inclusion in this review in that they reported MPOC data. RESULTS: Over the past 20 years MPOC has been used in settings additional to the children's treatment centres for which it was designed; used in 11 countries and translated into 14 languages; and used to measure change in respondents' perceptions over time. MPOC findings have also informed our understanding of the provision of family-centred services. Overall, parents report that service providers do a good job of providing respectful, comprehensive services in partnership with families, but that there remain limitations in the provision of general information, an area for improvement. Finally, MPOC has been shown to correlate with various other measures related to the provision of family-centred services. INTERPRETATION: The MPOC 'family' of measures can be used to assess both families' and service providers' experiences and perceptions of the family-centredness of services received/provided. Opportunities abound for further research enquiries.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".