Using Social Media While Waiting in Pain: A Clinical 12-Week Longitudinal Pilot Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain places an enormous burden on health care systems. Multidisciplinary pain management services are well documented as an effective means to improve patient outcomes. However, waiting lists to access these services are long and outcomes deteriorate. Innovative solutions such as social media are gaining attention as a way to decrease this burden and improve outcomes. It is a challenge to design research that demonstrates whether social media are acceptable to patients and clinically effective. OBJECTIVE: The aim was to conduct a longitudinal pilot study to understand what aspects of research design are key to the success of running a larger-scale study of social media use in the clinical management of chronic pain. METHODS: A 12-week study examined social media use by patients on the waiting list for the Royal Melbourne Hospital Pain Management Service. Selected social media resources were suggested for use by patients waiting for an appointment at the clinic. Patients filled out measures for pain interference and pain self-efficacy before and after the study. Follow-up was conducted at monthly intervals via telephone semistructured interviews to discuss engagement and garner individual perceptions towards social media use. A social media-use instrument was also administered as part of the after-study questionnaire. RESULTS: Targeted recruitment refined 235 patient referrals to 138 (58.7%) suitable potential participants. Contact was made with 84 out of 138 (60.9%) patients. After a further exclusion of 54 out of 84 (64%) patients for various reasons, this left 30 out of 84 (36%) patients fitting the inclusion criteria and interested in study participation. A final study cohort of 17 out of 30 (57%) was obtained. Demographics of the 17 patients were mixed. Low back pain was the primary condition reported as leading to chronic pain. Semistructured interviews collected data from 16 out of 17 (94%) patients who started the trial, and at final follow-up 9 out of 17 (53%) patients completed questionnaires. Low specificity of the resources to one's condition and time poorness may have been barriers to engagement. CONCLUSIONS: Results suggest that with refinements, this study design can be implemented successfully when conducting a larger social media study. At present, comment cannot be made on what effect using social media can have on patients on hospital waiting lists, nor whether those who use social media while waiting in pain achieve better outcomes from eventual participation in a chronic pain program. Long-term follow-up should be included in future studies to answer this. Future research should focus on multicenter randomized controlled trials, involving patients in the intervention design for improved participation and outcomes and for evidence to be sound.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".