Assessment of knowledge, attitude, and practices of research among faculties in medical college
Bibliographic record
Abstract
Medical research aims to advance knowledge, skills, and professionalism. Lack of research could lead to the demise of the profession as a viable discipline. Research orientation is a concept that incorporates four subscales and provides insight into faculties' overall perception of research. To assess the knowledge, attitude, and practices regarding research and to identify barriers for research among medical faculty. Our study is a questionnaire-based cross-sectional study covering 110 faculties of medical college. Data collection was done through the Edmonton research orientation survey (EROS), a pre-validated tool. EROS questionnaire consists of 50 questions in two sections –the first section containing demographic variables (12 questions) and the second section (consist of 38 items) asks the respondents to rate on a five-point Likert’s scale. A high response rate (90.9%) was achieved. Sixty-five percent of respondents achieved an overall medium EROS score and 33% of respondents achieved a high EROS score (mean Eros score 132.3+21.7) indicating high research orientation. Respondents showed high subscale scores: valuing research (63%) and being at the leading edge of the profession (66%). While involvement in research (47%) and evidence-based practice (53%) scored lower. The study highlighted important barriers like lack of time, skills and support. The results suggest that although faculties value research they engage less in carrying out and applying research. The positive research orientation provides an opportunity for the profession to use the available potential to increase research output.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".