Professional-Facing Digital Health Solutions for the Care of Patients With Chronic Pain: Protocol for a Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a highly prevalent condition and one of the most common reasons why people seek health care. As a result, chronic pain has a significant personal and economic burden. The COVID-19 pandemic has aggravated the situation for patients with chronic pain through increased risk factors (eg, anxiety or depression) as well as decreased access to health care. Digital health solutions to support people with chronic pain are becoming increasingly popular. Most of the research has focused on patient-facing digital health solutions, although it is clear that the involvement of health and care professionals is crucial in chronic pain care. Certainly, digital health solutions intended for the use of health and care professionals in the care of patients with chronic pain (ie, professional facing) exist, for example, for clinical decision support; however, no review has investigated the studies reporting these interventions. OBJECTIVE: The overall aim of this scoping review is to identify the available professional-facing digital health solutions for the purpose of chronic pain management. The objectives of this review are to investigate the components, target populations, and user settings of the available professional-facing digital solutions; health and care professionals' perspectives on using digital health solutions (if reported); the methods in which the digital health solutions are developed; and the outcomes of using professional-facing digital health solutions. METHODS: Databases including MEDLINE, Embase, CINAHL, PsycInfo, and Inspec will be searched for studies reporting professional-facing digital health solutions for chronic pain care, using a comprehensive search strategy developed for each of the specific databases. A total of 2 independent reviewers will screen the titles and abstracts for review inclusion and then conduct full-text screening. Any conflicts in study inclusion will be resolved by a third reviewer at each stage of the screening process. Following data extraction and quality assessment, a qualitative content analysis of the results will be conducted. This review will identify the available professional-facing digital health solutions for chronic pain management. The results of this review are likely to be heterogeneous in terms of content (ie, the digital solutions will serve a variety of purposes, settings, target populations, etc) and methods (ie, experimental and nonexperimental designs). RESULTS: The review is expected to finish in March 2024 and published in the summer of 2024. CONCLUSIONS: This protocol outlines the need for a scoping review to identify professional-facing digital health solutions for the management of chronic pain. Results from this review will contribute to the growing field of research into the utility of digital health for chronic pain management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51311.
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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 | 0.077 | 0.084 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.015 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".