Development and Validation of a Multidimensional Expectation Questionnaire for Thalassaemia Major Patients
Bibliographic record
Abstract
Nowadays, thalassaemia major (TM) patients are surviving into mature young adulthood; however, no published instrument exists to measure the expectations' dimensionality among older TM patients in their thirties. This study seeks to validate a novel multidimensional expectation questionnaire suitable for TM patients (MEQ-TMP) reaching their fourth decade of life. In order to establish the psychometric properties of the instrument, data analysis was carried out. The principal component analysis revealed four components ('Supportive social network'; 'Raising one's own family'; 'Career advancement'; 'Ability of daily activities'). Their cumulative contribution rate was 66.32%. Cronbach's alpha for the total scale was 0.87. Each subscale had an alpha value above 0.70; three subscales were in the 0.80 range. MEQ-TMP reliability was proved to be good. The known-group method served as a strategy in examining the operationalisation of the questionnaire's constructs. The present MEQ-TMP, developed for the aged group of TM patients, would be a useful tool for clinical personnel providing care to TM patients in understanding their outlook on life as they are growing up, to have better psychosocial adjustment to illness chronicity, live life as normally as possible, and fulfill their ambitions; thus enhancing their life satisfaction and quality of life.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".