An experience of workshop on introduction to statistical methods and SPSS hands-on training to enhance analytical skills among research professionals
Bibliographic record
Abstract
Background: In a current scenario, research project and writing a thesis is one of the most important components of PG and Ph.D. studies and a potential area where the students are challenged by lack of structured guidance. Thus the workshop on “Introduction to Statistical Methods & ‘SPSS’ Hands-on Training” was conducted with the objectives, to know the impact of workshop and to obtain suggestions for improvement.Methods: The workshop on “Introduction to Statistical Methods & ‘SPSS’ Hands-on Training” conducted during 7-9 November, 2016 by the Department of Community Medicine, Shri B. M. Patil Medical College, Hospital and Research Centre in collaboration with University of Manitoba, Canada. The effectiveness of the workshop was assessed by pre-and-post tests using Multiple-Choice Questions (MCQ). Analysis was done using paired t test and Wilcoxon signed rank test.Results: A total of thirty six participants attended the sessions. The overall participant opinion about the workshop was positive. Majority of the participants were female. Majority of the participants were in the age group of 30-35 years (33%), followed by 25-30 years (28%). Majority of participants were MBBS (31%), MD (28%), other degree faulty members (22%) and PhD (22%). The mean score in pre-and-post-test was 12.52±6.17 and 13.98±6.50 respectively (Range=2-27) and was found significant difference in the scores between pre-and-post-tests (p=0.002).Conclusions: The recommendations and suggestions given by workshop participants were to increase the duration of the workshop. Participants were satisfied with the teaching methodology in the workshops.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".