The Reliability of the Malay Versions of Hospital Anxiety Depression Scale (HADS) and Mcgill Quality of Life Questionnaire (MQOL) among a Group of Patients with Cancer in Malaysia
Bibliographic record
Abstract
Objective: This study intends to investigate the reliability, validity and patients’ perception towards the Malay Hospital Anxiety Depression Scale (HADS) and Malay McGill Quality of Life Questionnaire (MMQoL) in Terengganu cancer patients. Methods: It was conducted cross-sectionally in Hospital Sultanah Nur Zahirah (HSNZ), Kuala Terengganu, Malaysia recruiting 80 patients fulfilling the inclusion criteria. Socio-demographic data was analyzed descriptively and presented as frequencies. To examine patients’ perception towards the applicability and practicality, completion time, comprehension, comprehensiveness difficulty and instrusiveness of the instruments were inquired via a 5-item survey. For reliability purposes, the internal consistency reliability (Cronbach's a) was calculated while Spearman’s rank correlation coefficient (rs) was used to examine the strength of associations between and within instruments (convergent/divergent validity). Results: To the majority of patients, both HADS and MMQoL instruments were considered clear, comprehensive and not difficult to complete (completion time Conclusion: The overall findings suggested that both instruments have exhibited adequate evidence of reliability and validity plus being perceived as favourable for assessing health outcomes among cancer sufferers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".