Web-Based Learning for General Practitioners and Practice Nurses Regarding Behavior Change: Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Supporting patients to live well by optimizing behavior is a core tenet of primary health care. General practitioners and practice nurses experience barriers in providing behavior change interventions to patients for lifestyle behaviors, including low self-efficacy in their ability to enact change. Web-based learning technologies are readily available for general practitioners and practice nurses; however, opportunities to upskill in behavior change are still limited. Understanding what influences general practitioners' and practice nurses' adoption of web-based learning is crucial to enhancing the quality and impact of behavior change interventions in primary health care. OBJECTIVE: This study aimed to explore general practitioners' and practice nurses' perceptions regarding web-based learning to support patients with behavior change. METHODS: A qualitative, cross-sectional design was used involving web-based, semistructured interviews with general practitioners and practice nurses in Queensland, Australia. The interviews were recorded and transcribed using the built-in Microsoft Teams transcription software. Inductive coding was used to generate codes from the interview data for thematic analysis. RESULTS: In total, there were 11 participants in this study, including general practitioners (n=4) and practice nurses (n=7). Three themes emerged from the data analysis: (1) reflecting on the provider of the Healthy Lifestyles suite; (2) valuing the web-based learning content and presentation; and (3) experiencing barriers and facilitators to using the Healthy Lifestyles suite. CONCLUSIONS: Provider reputation, awareness of availability, resources, content quality, usability, cost, and time influence adoption of web-based learning. Perceived quality is associated with culturally tailored information, resources, a balance of information and interactivity, plain language, user-friendly navigation, appealing visual presentation, communication examples, and simple models. Free web-based learning that features progress saving and module lengths of less than 2 hours alleviate perceived time and cost barriers. Learning providers may benefit by including these features in their future behavior change web-based learning for general practitioners and practice nurses.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".