Research productivity of junior academic staff at a tertiary medical college in south west, Nigeria.
Bibliographic record
Abstract
BACKGROUND: The research productivity of medical faculty has been well studied in developed countries, unlike in the developing countries. OBJECTIVES: This study proposes to assess the level of research productivity over a 2 year period and identify the challenges to conducting research among junior academic staff of the College of Medicine, University of Lagos. METHODS: An observational cross-sectional study in which the 120 junior academic staff from both basic sciences and clinical sciences were evaluated between January and September 2005. Data collection was by self-administered questionnaires distributed to the study population. RESULTS: There were 83 (69.1%) respondents comprising 38 males (45.6%) and 45 females (54.2%). The median age group was 31-40 years. Most respondents (57, 83%) spent less than 10 hours/week on research. On average they had completed 3-4 scholarly articles within the past 2 years. Nineteen (21.7%) of the subjects were considered to have optimal research productivity having completed over 5 scholarly research papers. The lecturers with optimal research productivity were significantly more likely to be male, and spent over 10 hours a week in hospital related clinical and laboratory related activities. (p = 0.02, and p = 0.03). Inadequate funding and laboratory facilities, and poor technological infrastructure were the most common causes of impediments to research reported by 78%, 69% and 55% of the lecturers respectively. CONCLUSION: Optimal research productivity was seen in about one quarter of the study population and was associated with male gender and prolonged duration of clinical/laboratory activities. Negligible research financing and poor laboratory support were major impediments to research productivity.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".