Adaptation, further development and evaluation of the measurement properties of the person-centred community care inventory (PERCCI-S) for use in the Swedish municipal health care system
Bibliographic record
Abstract
BACKGROUND: Successful implementation and sustainability of person-centred care (PCC) require continuous evaluations and valid measurements. While several instruments measure patients' experience with PCC, to our knowledge, no validated instrument exists in Swedish for use in home-based primary care (HBPC). This study aimed to adapt and further develop an instrument for measuring patients' experiences of person-centred care in HBPC for use in the Swedish municipal health care system, with a 12-item version of the Person-Centred Community Care Inventory (PERCCI) used as a starting point. Furthermore, its content and measurement properties were evaluated via a mixed-methods approach involving item response theory and qualitative content analyses. METHODS: This study was conducted in two stages. First, the PERCCI 12 item version was translated into Swedish using a forward-backward approach. Content validity was evaluated through focus groups with 24 registered nurses and managers, resulting in revisions. Second, the revised version (PERCCI-S) was psychometrically evaluated via two rounds of postal questionnaires (2022; 2023) with patients 18 years or older receiving municipal HBPC in Sweden (n = 1,171; n = 1,429). The psychometric evaluation involved factor analyses and item response theory analyses to assess dimensionality, item difficulty and discrimination, item and test information, test‒retest reliability, internal consistency reliability, as well correlational analyses of convergent and discriminant validity. Content validity was further assessed through a panel review with experts (n = 7) and cognitive interviews with patients (n = 20). RESULTS: = 0.96). Test-retest reliability showed good temporal stability (r = 0.79, n = 96). The content validity index was 1.0, indicating that all the items were relevant. However, the scale's discriminant validity was unsatisfactory, with 18.0% of respondents having the highest score. CONCLUSIONS: The psychometric evidence of the PERCCI-S provides support for its use in the Swedish municipal HBPC. Future studies should test different response formats in an effort to reduce ceiling effects.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".