The Effectiveness of an Online Learning Strategy on Changing Physical Activity Counseling Practice in Nurses
Bibliographic record
Abstract
Background Nurses may be well poised for providing physical activity guidance and support to patients. Purpose The purposes of this study were to examine the effectiveness of a concise, evidence based online learning modules strategy (OLMS) for improving nurses’ physical activity counselling. Methods 68 nurses were randomly assigned to either an OLMS group or control group. The OLMS group completed a series of six online learning modules aimed at improving physical activity counselling practice. Results The OLMS group, compared to the control group, showed a trend for improvement in Physical Activity Counselling Practice ( p = .063) after controlling for baseline values, and significant improvement in (a) Self-efficacy for Physical Activity Counselling ( p = .001), (b) Knowledge of Physical Activity Guidelines, ( p = .031), and (c) Perceived Benefits of Physical Activity Counselling ( p = .014) over the course of the intervention. No significant change was found for Barriers for Providing Physical Activity Counselling ( p > .05). Conclusions The OLMS tested may be an effective means for improving self-efficacy, knowledge, and perceived benefits of physical activity counselling, suggesting the utility of online learning strategies for improving nurses’ physical activity counselling practice. Given barriers to providing physical activity counselling were not affected by the intervention, future interventions and policy change could target these barriers specifically in order to give nurses more tools and time for reaching patients and addressing physical activity counselling in practice.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".