Are people getting quality thalassemia care in twin cities of Pakistan? A comparison with international standards
Bibliographic record
Abstract
OBJECTIVES: This study was conducted to determine if thalassemia patients were getting quality care in Rawalpindi and Islamabad, Pakistan, as per international standards and to identify determinants for better quality of thalassemia care. DESIGN: A cross sectional study was conducted using interview based structured questionnaire, which was developed using standards of thalassemia care used by International Thalassemia Foundation. SETTING: Five healthcare facilities catering to the needs of thalassemia patients in Rawalpindi and Islamabad, Pakistan. PARTICIPANTS: Data were collected from 315 thalassemia patients from May to August, 2016. MAIN OUTCOME MEASURE: Survey data on quality indicators. RESULTS: Results showed that almost half of thalassemia patients (48.5%) were getting poor quality of care. On average patients were getting only 63.93% of possible quality care for the disease. The most deficient quality area was management of complications where patients were getting only 49.1% of possible care. Better quality of care was likely to be received by those patients who were educated, patients with educated fathers, those visiting private facilities and those who were visiting facilities in Islamabad. Those with concomitant diseases were also likely to receive better care. CONCLUSION: Quality of care provided to thalassemia patients was well below the international standards for the care of thalassemia. There is a need to take urgent action to improve quality of care in the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".