Validity of the Diabetes, Hypertension and Hyperlipidaemia (DHL) Knowledge Instrument among Medical Students of Karachi
Bibliographic record
Abstract
BACKGROUND: While there have been a number of studies on DM, hypertension and hyperlipidaemia, an instrument which assesses knowledge based on all three conditions has neither been established nor authorized in Pakistan. Hence, the focus of this study was to establish a pre- tested extensive questionnaire to evaluate medical students’ understanding of DM, hypertension, hyperlipidaemia and their medications for use.METHODS: A pre-validated and pre-tested DHL instrument was employed on 250 students of Dow Medical and Sindh Medical College and on 45 physicians working in a leading teaching hospital of Karachi. The DHL knowledge instrument was then distributed a second time to the very same set of students, after a period of 2 months, at the end of the foundation module, once they had received some basic formal medical education including diabetes and CVS diseases.RESULTS: The overall internal consistency for the DHL instrument failed to comply with the set standard of more than or equal to 0.7 as our results yielded Cronbach’s α of 0.6. Overall the average difficulty factor of 28 questions is 0.41, which highlighted that the instrument was moderately tough. The mean scores for all domains were substantially lower in the students section in comparison to that of the professional section, which had remarkable impact on the overall mean(SD) knowledge score (40.58 ± 14.63 vs. 63.49 ± 06.67 ; p value = 0.00).CONCLUSION: The instrument can be used to recognize people who require educational programs and keep an account of the changes with the passage of time as it could help in differentiating the knowledge levels among its participants based on their educational status.
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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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".