Design and Validation of Questionnaires Investigating Access and Utilization of Cervical Cancer Treatment and Palliative Care
Bibliographic record
Abstract
BACKGROUND: Standardized tools to evaluate access and utilization of cervical cancer treatment and care remain scarce in developing countries. The objective of this study was to validate questionnaires to investigate access and uptake of cervical cancer treatment and palliative care. MATERIALS & METHODS: We designed and validated two questionnaires for patient and community and health worker surveys to determine the main constructs of each of the draft questionnaires. Pilot data was collected randomly amongst 50 patient and community participants and 14 health workers respectively in Chitungwiza, Zimbabwe. Content and face validity were assessed qualitatively from expert evaluations. Construct validity, reliability and internal consistency testing were conducted using exploratory factor analysis and Cronbach’s alpha correlation coefficient respectively. RESULTS: Twelve (12) experienced researchers, based on convenience, reviewed the questionnaires and validated their draft constructs based on experience and literature. Each of the questionnaires was sub-divided into 4 separate mini-questionnaires respectively. All the eight mini-questionnaires were analyzed independently and Kaiser-Meyer-Olkin coefficients ranged from 0.5-0.9 and Bartlett’s sphericity tests were all significant, p<0.001, showing promising good constructs. Patient and community questionnaire had 15 meaningful constructs while the health worker questionnaire had 13. Cronbach’s alpha (α) coefficients for internal consistency reliability testing of all the final constructs were greater than the minimum acceptable threshold of 0.70. CONCLUSION: This analysis revealed the validity and reliability of questionnaires that could be used to evaluate access and utilization of cervical cancer treatment and palliative care in countries affected by the disease.
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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 | 0.076 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".