An analysis of interactive hands-on workshops on medical writing.
Bibliographic record
Abstract
OBJECTIVE: To assess the improvement in participant's knowledge and skills pertaining to medical writing by interactive hands-on workshops. METHODS: During the course of three months (January to March 2009), four interactive 5 hours hands-on workshops were organized on Medical Writing. All participants completed a pre-workshop and post-workshop questionnaire. Fourteen questions were included in both questionnaires related to workshop outline. Eight questions were related to knowledge of the participants about different aspects of medical writing (yes/no). Participants were also asked six questions to rate their skills relating to medical writing on a numerical scale of 1-5 (1: no skills and 5: expert). Participant's feedbacks were also analyzed. The pre-workshop and post-workshop responses were compared to see if there was any significant difference by using McNemar test and paired-t test where appropriate. RESULTS: Response to eight questions regarding knowledge (authorship criteria, types of data, application of significance test, search techniques, plagiarism, Vancouver style of reference and copyright statement) showed that there was a significant difference in all responses (p < 0.005). Same trend was observed in skills rating (literature search, basic data analysis, writing an original article, writing references, paper submission for publication) of participants themselves before and after the workshop (p < 0.0001). Analysis of feedback showed that participants found the workshop informative, practical and helpful in improvement of their skills for paper writing. CONCLUSION: Short interactive hands-on medical writing workshops are helpful and beneficial in improving the knowledge and skills of the participants.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".