European general practitioners’ attitudes towards person-centred care and factors that influence its implementation in everyday practice: The protocol of the cross-sectional PACE GP/FP study in 24 European countries
Bibliographic record
Abstract
BACKGROUND: Person-centred care (PCC) is a fundamental principle in general practice, emphasising practices tailored to individual patient preferences, needs, and values. Despite the importance of PCC, general practitioners (GPs) face obstacles in effectively implementing it, with associated factors remaining unclear. OBJECTIVES: The PACE GP/FP study aims to explore GPs' attitudes towards PCC and the factors facilitating or hindering its implementation in daily practice across European countries. This paper outlines the PACE GP/FP study protocol. METHODS: an online survey distribution to GPs in 24 European countries. Study instruments include two validated questionnaires (Perceived Stress Scale (PSS) and Patient Physician Orientation Scale (PPOS)) and additional items covering general information about the doctor and their practice, as well as facilitators and barriers to PCC. These additional items were specifically developed for the study, translated using the forward-backward method, evaluated through cognitive debriefing, and integrated into the REDCap platform to create language and country-specific survey links. The STROBE checklist guides the reporting of the manuscript. CONCLUSION: The PACE GP/FP study will provide a comprehensive exploration of GPs' attitudes towards PCC and the factors shaping its practice in Europe. The findings from the PACE GP/FP study will provide evidence for designing future implementation strategies and guide targeted interventions to promote PCC in primary care across Europe.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".