Development and Evaluation of a Research Methods Course in Protocol Writing for Learners in a Master of Public Health Program
Bibliographic record
Abstract
Training in research methods is important for improvement of healthcare delivery and population outcomes. Graduate programs of public health play a critical role in offering such education to current and future healthcare professionals as well as entry level learners with no experience in the field. A key skill across all fields of research methods and public health practice is protocol writing. It is unknown if teaching students research methods through protocol writing is a successful strategy and whether students find it to be helpful as they pursue health professions. The objective of this study was to describe the design and evaluation of a research methods course focused on protocol writing among students enrolled a Masters of Public Health Program. A case report design including description of course content, method of evaluation, and course delivery are provided. The setting was the Population and Public Health Research Methods course at a publicly funded institution in Canada. The first three cohorts of students (2016-2018) enrolled in the course were evaluated during the course period and six months after completing the course. A total of 51 students completed the survey, and the majority were students were very or extremely satisfied with the course. Overall students expressed that the course well-prepared them for their practicum or thesis work and post-graduation plans. Findings suggest that using protocol writing as a tool for teaching research methods was well-received by students and prepared them for both their potential career paths and for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.108 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".