Development and validation of a psychometric scale to assess attitude towards safe abortions in Pakistan
Bibliographic record
Abstract
Despite severe health and economic consequences that women face because of the negative attitude of healthcare providers towards safe abortion and post-abortion care (SA/PAC), no psychometric tool has yet been validated for assessing the attitude towards SA/PAC. Only a handful of studies have attempted to assess healthcare providers' attitude towards safe abortions in Pakistan. Therefore, this study aimed to develop and validate a psychometric scale to assess attitude towards safe abortions in Pakistan. The study collected data from 106 workers of an NGO that provides SA/PAC through an online and anonymous survey using the organisation's network. The study used factor analytic techniques and structural equation modelling to validate the factor structure and a final hierarchical model. A final scale of seven items relating to attitude towards elective abortions and moral attitude towards safe abortions was validated. The scales were highly reliable with both factors having reliability indicators greater than 0.7. The scale can be easily implemented to assess providers' attitude towards safe abortions. This will allow programmers to screen healthcare providers with a negative attitude, and evaluate the efficacy of their Value Clarification and Attitude Transformation (VCAT) programmes that are aimed at transforming providers' attitude towards safe abortions.
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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) | 0.000 | 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) | 0.003 | 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".