The use of social media by arthritis health professionals to disseminate a self-management program to patients: A feasibility study
Bibliographic record
Abstract
Objective The objective of this study was to determine the feasibility of Facebook as a dissemination strategy for the People Getting a Grip on Arthritis self-management program by arthritis health professionals to their patients. Methods The feasibility study comprised a single arm, pre-post design that included a convenience sample of 78 arthritis health professionals across Canada. Assessments were performed at baseline, two-weeks post-intervention, and at three-months follow-up using online questionnaires. The primary outcome measure was change in perceived usability of Facebook as a dissemination strategy for the People Getting a Grip on Arthritis program with patients at two-weeks post-intervention using an instrument based on an extended version of the Technology Acceptance Model 2. Comparisons with baseline were assessed using t-test analyses. Results Statistically significant improvements from baseline were seen for all items of the Technology Acceptance Model 2 domains: perceived ease of use (four items), intention to use (two items) and output quality (two items) domains. Variable results were seen for the job relevance, perceived usefulness, voluntariness, and result demonstrability domains of the Technology Acceptance Model 2. There were no statistically significant improvements for the subjective norm and image domains. Conclusions Facebook may provide arthritis health professionals with an additional option of how to best share evidence-based information to allow their patients to successfully self-manage their arthritis.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".