Exploratory factor analysis and reliability of the Primary Health Care Engagement (PHCE) Scale in rural and remote nurses: findings from a national survey
Bibliographic record
Abstract
Aim The study purpose was to provide evidence of validity for the Primary Health Care Engagement (PHCE) Scale, based on exploratory factor analysis and reliability findings from a large national survey of regulated nurses residing and working in rural and remote Canadian communities. BACKGROUND: There are currently no published provider-level instruments to adequately assess delivery of community-based primary health care, relevant to ongoing primary health care (PHC) reform strategies across Canada and elsewhere. The PHCE Scale reflects a contemporary approach that emphasizes community-oriented and community-based elements of PHC delivery. METHODS: Data from the pan-Canadian Nursing Practice in Rural and Remote Canada II (RRNII) survey were used to conduct an exploratory factor analysis and evaluate the internal consistency reliability of the final PHCE Scale. Findings The RRNII survey sample included 1587 registered nurses, nurse practitioners, licensed practical nurses, and registered psychiatric nurses residing and working in rural and remote Canada. Exploratory factor analysis identified an eight-factor structure across 28 items overall, and good internal consistency reliability was indicated by an α estimate of 0.89 for the final scale. The final 28-item PHCE Scale includes three of four elements in a contemporary approach to PHC (accessibility/availability, community participation, and intersectoral team) and most community-oriented/based elements of PHC (interdisciplinary collaboration, person-centred, continuity, population orientation, and quality improvement). We recommend additional psychometric testing in a range of health care providers and settings, as the PHCE Scale shows promise as a tool for health care planners and researchers to test interventions and track progress in primary health care reform.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".