Exploring perspectives of type 2 diabetes prevention program coaches and training delivery staff on e-learning training: a qualitative study
Bibliographic record
Abstract
BACKGROUND: E-learning can be an effective and efficient mode of training healthcare practitioners. E-learning training for diabetes prevention program coaches was designed and developed with input from end users. Insight from those who deliver the training and coaches who have taken the training can provide critical feedback for further refinement of the e-learning training. The purpose of this study was to understand diabetes prevention coaches' (i.e., those taking the training) and training delivery staffs' (i.e., those overseeing the training) perspectives of the coach e-learning training. Individuals wishing to become diabetes prevention program coaches were required to complete and pass the e-learning training to become a certified coach. METHODS: A pragmatic paradigm guided the methodology for this study. Semi-structured interviews were conducted with a purposive sample of diabetes prevention program coaches (n = 8) and diabetes prevention program training staff (n = 3). Interviews were recorded, transcribed verbatim, and analyzed using template analysis. Themes were separately constructed from coach and staff data. RESULTS: There were seven high order themes constructed from the coach data: (a) training design, (b) "I didn't know what to expect from the training", (c) technology usability, (d) learning, (e) coaches' backgrounds shaped their training experience, (f) support, and (g) coaches valued the training. Two high order themes were constructed from the staff interviews: (a) streamlining the training delivery, and (b) ensuring coaches meet the diabetes prevention program standard. CONCLUSIONS: This study highlights the importance of exploring perspectives of both those receiving and delivering e-learning training to refine content and processes. Qualitatively evaluating the delivery of e-learning training and modifying the training based on the evaluation results can lead to a more acceptable, efficient, and effective e-learning training. Coaches and staff emphasized the benefits of having high-quality online components, and that the brief training promoted gains in knowledge and improvements in skills. Resultswere used to inform modifications to the coach e-learning training for this diabetes prevention program and can be used to inform other healthcare practitioner e-learning trainings.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".