Svensk version av Canadian Physiotherapists Arthritis Care Survey – validering och test-retest reliabilitet för användning i primärvården
Bibliographic record
Abstract
Patients with arthritis are often treated in a primary care setting. To study perceived knowledge and skills in arthritis care the questionnaire "Swedish version of the Canadian Physiotherapists Arthritis Care Survey" has been developed for use in rheumatology specialist care. The aim was to study whether the questionnaire were valid and reliable also for use in primary care. To assess face and content validity a focus group of eight physiotherapists working in primary care served as an expert panel. Test-retest was studied in a group of 30 physiotherapists working in primary health care and calculated with ICC /kappa. After some revision, the focus group gave the questionnaire face and content validity. Based on the focus group discussions questions not supported by the aim were removed (content of rheumatology training, certification and extended scope of practice) and four items added concerning recent education and perceived needs in the field. 95% (122/128) of \nthe questions in the revised questionnaire achieved a "fair to good" or "excellent" test-retest reliability (37% and 58%), while 5% (6/128) were classified as "poor". In summary, the questionnaire showed acceptable validity and reliability for most questions. Some of the questions need revision before the questionnaire is used in primary care.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".