Testing the comparability and interpretability of the revised professional practice environment scale – Filipino version
Bibliographic record
Abstract
Background and aims: The Revised Professional Practice Environment (RPPE) Scale is a 39-item four Likert scale-rated questionnaire. The US-based Massachusetts General Hospital developed it as a measure of nurses’ leadership and autonomy over practice, relationship with physicians, control over practice, communication about patients, teamwork, handling of disagreement and conflict, internal work motivation, and cultural sensitivity. The RPPE Scale has been translated into several languages but Filipino. The aim of this paper was to translate the RPPE Scale to the Filipino language in order to establish an initial evidence for construct equivalence between it and the original version.Methods: Methodological design was used in the study following a four-step translation process. The data collection commenced in 2020.Results: The RPPE scale was subjected to forward translation in Filipino language. It was then back translated into English after which the conceptual equivalence was determined for similarity of translation and comparability of interpretation. The results based on weighted means were highly similar and highly comparable.Conclusions: The RPPE-Filipino version demonstrated an acceptable evidence of language- and culture-specificity that is sufficiently robust for use in Philippine setting. The existence of an instrument that is comparable and similar to the original RPPE Scale paves the way for initiating nursing staff development programs that are based on the tenets of professional practice environment.
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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 | 0.049 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".