Research productivity among faculty members at medical and health schools in Saudi Arabia. Prevalence, obstacles, and associated factors.
Bibliographic record
Abstract
OBJECTIVE: To identify the prevalence, factors and obstacles affecting research productivity among academic staff at medical and health colleges in the Kingdom of Saudi Arabia. METHODS: This cross-sectional survey employed self-administered questionnaires to collect data on faculty members' profile, research activities, and obstacles impeding research productivity. The questionnaires were distributed randomly to 500 faculty members, of which 389 (77.8%) completed the questionnaire at 10 medical and health colleges during January to April 2011. The data were analyzed and presented in a descriptive fashion. RESULTS: Only 150 (38.6%) respondents reported published work in the past 2 years. Of these, 80% indicated sole-authors research and around a quarter (26%) reported co-authors work. Males and young faculty members were more likely to publish research than their counterparts. Faculty members who reported involvement in administrative activities were less likely to publish. Those who reported supervising postgraduate students or had attained training on research methods were more likely to produce research. Respondents perceived that lack of time, lack of research assistants, lack of funds for research, and being busy with teaching load were the most cited obstacles impeding research productivity. CONCLUSION: Understanding factors and barriers impeding research productivity is a prerequisite for interventions that are directed to promote health services research among faculty members in medical schools.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".