Bibliographic record
Abstract
Brief counseling, when provided by adequately trained nurses, can motivate and support patient health behavior change. However, numerous barriers can impede nurses' capability and motivation to provide brief counseling. Theory-based interventions, as well as information and communication technologies, can support evidence-based practice by addressing these barriers. The purpose of this study was to document the development process of the E_MOTIV asynchronous, theory-based, adaptive e-learning program aimed at supporting nurses' provision of brief counseling for smoking cessation, healthy eating, and medication adherence. Development followed French's stepwise theory- and evidence-based approach: (1) identifying who needs to do what, differently, that is, provision of brief counseling in acute care settings by nurses; (2) identifying determinants of the provision of brief counseling; (3) identifying which intervention components and mode(s) of delivery could address determinants; and (4) developing and evaluating the program. The resulting E_MOTIV program, guided by the Theory of Planned Behavior, Cognitive Load Theory, and the concept of engagement, is unique in its adaptive functionality-personalizing program content and sequence to each learners' beliefs, motivation, and learning preferences. E_MOTIV is one of the first adaptive e-learning programs developed to support nurses' practice, and this study offers key insights for future work in the field.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".