Consolidating a program theory on how social media supports health care providers’ knowledge use in clinical practice: a realist-informed qualitative study
Bibliographic record
Abstract
Abstract Objective: This study aimed to consolidate a program theory, developed from a previous realist review, to further understand how and under what circumstances social media supports health care providers’ knowledge use. Methods: A realist-informed qualitative study was conducted. We carried out in-depth interviews with 11 participants, including content developers and health care providers from China, Australia, and Canada. The data analysis was informed by categorizing and connecting strategies. Results: Ten context-mechanism-outcome (CMO) configurations were developed to consolidate the program theory. Among these, 4 CMOs confirmed the original CMOs from the realist review, 4 refined the original ones, and 3 were new propositions. These 10 CMOs were situated within 4 interconnected levels of outcomes: social media products, access, engagement, and knowledge use. They considered (1) content developers’ capabilities and capacities, (2) health care providers’ increased attention, (3) fulfillment of information needs, (4) access to social influence and support, (5) perception of message value and implementability, (6) behavior capabilities, self-efficacy, intention, and awareness, and (7) ability to exercise professional autonomy as the key mechanisms. We developed the consolidated program theory based on the 10 CMOs. Conclusions: Social media can promote knowledge use by health care providers. Future empirical studies drawing on the program theory need to be conducted to further optimize the theoretical understanding.
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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 | 0.093 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".