Developing and assessing a tool to measure motivation among physicians in Lahore, Pakistan
Bibliographic record
Abstract
Physicians' motivation plays a vital role in health systems particularly in dense and urban cities, which deal with high volumes of patients in a variety of settings. The loss of physicians due to low motivation to developed countries is also a critical aspect affecting the quality of care in many regions. Fewer studies have explored health provider and particularly physicians' motivation in developing countries, which is critical to health service delivery. In addition, limited relevant tools have been developed and tested in low and middle-income settings like Pakistan. The purpose of this study was to create and test a tool for measuring physician motivation. A tool was developed to explore physicians' motivation in the Lahore district, Pakistan. Three sections of the questionnaire, which included intrinsic, socio-cultural and organizational factors, were tested with a stratified, random sample of 360 physicians from the public and private health facilities. Factor analysis produced six factors for 'intrinsic motivation,' seven for 'organizational motivation' and three for 'socio-cultural motivation' that explained 47.7%, 52.6% and 40.6% of the total variance, respectively. Bartlett's test of sphericity and the KMO were significant. Cronbach's α and confirmatory factor analysis were found satisfactory for all three sections of questionnaires. In addition to identifying important intrinsic, socio-cultural and organizational factors study found the questionnaires reliable and valid and recommend further testing the applicability of the instrument in similar and diverse settings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".