Designing and validating of a questionnaire measuring perceived self-care ability (PSCA) in chronic stroke patients at home
Bibliographic record
Abstract
BACKGROUND: Patients with a stroke often cannot care for themselves after hospital discharge. Assessment of their self-care ability is the first step in planning post-discharge home care. This study aimed to design and validate a measure of perceived self-care ability (PSCA) in stroke patients. METHODS: A sequential-exploratory mixed method was conducted in Tehran, Iran, in 2020-2021. The qualitative phase involved in-depth semi-structured interviews with 12 participants. Transcripts were content analyzed. The results guided the development of 81 items. psychometric properties such as face validity (Impact Score > 1.5), content validity ratio (CVR > 0.63), content validity index (Item Content Validity Index: ICVI > 0.78, Scale Content Validity Index/Average: SCVI/Ave > 0.8) and Kappa value (Kappa > 0.7), internal consistency (Cronbach's alpha > 0.7), relative reliability (ICC: inter class correlation coefficient), absolute reliability (Standard Error of Measurement: SEM and Minimal Detectable Changes: MDC), convergent validity (Correlation Coefficient between 0.4-0.7), interpretability, responsiveness, feasibility, and ceiling and floor effects were assessed. RESULTS: Content analysis of the qualitative interviews yielded 5 major categories and 9 subcategories that reflected "Perceptual stability", "Cognitive fluctuations", "Sensory, Motor and Physical health"," The subjective nature" and "The dynamic nature" of PSCA. Results of face and content validity reduced the number of items to 32, capturing three dimensions of PSCA in chronic stroke patients; these dimensions included perceptual ability, threatened health status, and sensory, motor, and cognitive ability. The findings supported the reliability and validity of the measure. CONCLUSIONS: The PSCA questionnaire was developed and validated within the Iranian culture. It is useful in assessing the self-care of patients with stroke and in informing practice.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".