The use of information and communication technologies by arthritis health professionals to disseminate a self-management program to patients: a pilot randomized controlled trial protocol
Bibliographic record
Abstract
Design and objective: This paper describes the protocol for a three-arm, single-blind, parallel design randomized controlled trial (RCT) to investigate the perceived usability of Facebook to share information from an evidence-based arthritis self-management program with patients compared with email or an educational website after two weeks. Study population Three-hundred and twenty-seven arthritis health professionals (i.e., nurses or physical/occupational therapists) registered with their regulatory body in Canada, currently practicing clinically defined as spending a minimum of 50% of their time (working week) in direct arthritis patient care. Interventions The proposed RCT will include three information and communication technology (ICT) intervention groups: Facebook, email, and an educational website. Outcome measures The primary outcome will be perceived usefulness by health professionals of using the ICT intervention to share information with their patients according to the technology acceptance model 2 (TAM2) questionnaire at two weeks post-intervention. Secondary outcomes will include other usability domains of the TAM2 questionnaire (i.e., perceived ease of use, result demonstrability, output quality, job relevance, image, voluntariness, subjective norm, and intention to use) at two weeks, three months, and six months post-intervention. Analysis: An analysis of variance will be conducted to compare TAM2 questionnaire scores of the Facebook group with the email and educational website groups.
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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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".