Adaptation of the Sinclair Compassion Questionnaire Into Turkish: A Validity and Reliability Study
Bibliographic record
Abstract
This study was conducted to adapt the Sinclair Compassion Questionnaire into Turkish and to test its validity and reliability. The sample of the methodological study consisted of 390 patients who were hospitalized in internal and surgical clinics. The data were collected between May 2022 and December 2022 using the Personal Information Form, Newcastle Nursing Care Satisfaction Scale and Sinclair Compassion Questionnaire. The Sinclair Compassion Questionnaire consists of 15 items, containing a single latent compassion factor. Validity analysis included content validity index, concurrent validity, convergent validity, exploratory factor analysis, confirmatory factor analysis, reliability analysis included test-retest and reliability analysis using Cronbach’s alpha reliability coefficient. The content validity index of the scale was found to be between 0.88 and 1.00. Confirmatory factor analysis χ 2 = 283.754, dF = 71, RMSEA = 0.078, indicating a good/excellent fit for the model. The item means of the scale ranged between 4.43 ± 0.60 and 4.10 ± 0.47, and the factor loadings ranged between 0.672 and 0.824. Convergent validity is at an acceptable level ( r = 0.674). The Cronbach’s alpha coefficient of the scale was 0.94. The Sinclair Compassion Questionnaire is a valid and reliable instrument to assess patients’ experiences of compassion in Turkish populations with acute and chronic diseases.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".