Assessment of the validity and reliability of the Decisional Conflict Scale for pregnant women in Iran
Bibliographic record
Abstract
Background: Engaging pregnant women in selecting the delivery type has been recognized as an important factor for world health. The aim of this study was to assess the validity and reliability of the Iranian version of Low Literacy Decisional Conflict Scale (DSC-LL) in Iran. Methods: The English version of DCS-LL was translated and administered to 54 women eligible for selecting the type of delivery. The quantity content validity, the Content Validity Rate (CVR) and Content Validity Index (CVI) were examined. The reliability of the scale was assessed by two methods of internal consistency and test–retest via intra-class correlation coefficient, and Pearson correlation coefficient. Results: All 10 items had CVR points ranging from 0.8 to 1.0. The scores on the four subscales of this scale revealed high internal consistency (Cronbachchr('39')s alpha= 0.847). Test-retest reliability via Intraclass Correlation Coefficient (ICC) (ICC=0.981) and Pearson’s correlation coefficient (r=0.083) was significant at the level of P<0.001. Conclusion: The results showed that the Iranian version of DCS-LL is a valid, reliable and appropriate tool to be administered to pregnant women for selecting the type of delivery. However, further studies are needed to evaluate the influence of health literacy on this scale.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".