Improving Primary Health Care in Chronic Musculoskeletal Conditions through Digital Media: The PEOPLE Meeting
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal (MSK) conditions are the most common cause of severe chronic pain and disability worldwide. Despite the impact of these conditions, disparity exists in accessing high quality basic care. As a result, effective treatments do not always reach people who need services. The situation is further hampered by the current models of care that target resources to a limited area of health services (eg, joint replacement surgery), rather than the entire continuum of MSK health, which includes services provided by primary care physicians and health professionals. The use of digital media offers promising solutions to improve access to services. However, our knowledge in this field is limited. To advance the use of digital media in improving MSK care, we held a research planning meeting entitled "PEOPLE: Partnership to Enable Optimal Primary Health Care by Leveraging Digital Media in Musculoskeletal Health". This paper reports the discussion during the meeting. OBJECTIVE: The objective of this study was to: (1) identify research priorities relevant to using digital media in primary health care for enhancing MSK health, and (2) develop research collaboration among researchers, clinicians, and patient/consumer communities. METHODS: The PEOPLE meeting included 26 participants from health research, computer science/digital media, clinical communities, and patient/consumer groups. Based on consultations with each participant prior to the meeting, we chose to focus on 3 topics: (1) gaps and issues in primary health care for MSK health, (2) current application of digital media in health care, and (3) challenges to using digital media to improve MSK health in underserviced populations. RESULTS: The 2-day discussion led to emergence of 1 overarching question and 4 research priorities. A main research priority was to understand the characteristics of those who are not able to access preventive measures and treatment for early MSK diseases. Participants indicated that this information is necessary for tailoring digital media interventions. Other priorities included: (1) studying barriers and ethical issues associated with the use of digital media to optimize MSK health and self-management, (2) improving the design of digital media tools for providing "just-in-time" health information to patients and health professionals, and (3) advancing knowledge on the effectiveness of new and existing digital media interventions. CONCLUSIONS: We anticipate that the results of this meeting will be a catalyst for future research projects and new cross-sector research partnerships. Our next step will be to seek feedback on the research priorities from our collaborators and other potential partners in primary health care.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".