The Public and Patient Engagement Evaluation Tool: forward-backwards translation and cultural adaption to Norwegian
Bibliographic record
Abstract
BACKGROUND: Patient engagement is recommended for improving health care services, and to evaluate its organisation and impact appropriate, and rigorously evaluated outcome measures are needed. METHODS: Interviews (N = 12) were conducted to assess relevance of the Canadian Public and Patient Engagement Evaluation Tool (PPEET) in a Norwegian setting were performed. The tool was translated, back translated, and assessed following cognitive interviews (N = 13), according to the COSMIN checklist. Data quality was assessed in a cross-sectional survey of patient advisory board members from different rehabilitation institutions (N = 47). RESULTS: Interviews with patient board representatives confirmed the relevance of the PPEET Organisational questionnaire in a Norwegian setting and contributed five additional items. Translation and back translation of the original PPEET showed no major content differences. Differences in vocabulary and sentence structure were solved by discussion among the translators. Comments from cognitive interviews mainly related to the use of different synonyms, layout, and minor differences in semantic structure. Results of the cross-sectional survey support the data quality and construct validity of PPEET items, including 95 score comparisons where 76 (80%) were as hypothesized. CONCLUSIONS: The PPEET Organisational questionnaire has been thoroughly translated and tested, and the resulting Evalueringsverktøy for Brukermedvirkning (EBNOR) has adequate levels of comprehensibility and content validity. Further testing for measurement properties is recommended, but given these results, the EBNOR should be considered for assessing patient engagement in a Norwegian health care organisational context.
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 | 0.044 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".