Physician perspectives on chronic pain management: barriers and the use of eHealth in the COVID-19 era
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a highly prevalent and disabling condition which is often undertreated and poorly managed in the community. The emergence of COVID-19 has further complicated pain care, with an increased prevalence of chronic pain and mental health comorbidities, and burnout among physicians. While the pandemic has led to a dramatic increase in virtual health care visits, the uptake of a broader range of eHealth technologies remains unclear. The present study sought to better understand physicians' current needs and barriers in providing effective pain care within the context of COVID-19, as well as gauge current use, interest, and ongoing barriers to eHealth implementation. METHODS: A total of 100 practicing physicians in British Columbia, Canada, completed a brief online survey. RESULTS: The sample was comprised of physicians practicing in rural and urban areas (rural = 48%, urban = 42%; both = 10%), with the majority (72%) working in family practice. The most prominent perceived barriers to providing chronic pain care were a lack of interdisciplinary treatment and allied health care for patients, challenges related to opioid prescribing and management, and a lack of time to manage the complexities of chronic pain. Moreover, despite expressing considerable interest in eHealth for chronic pain management (82%), low adoption rates were observed for several technologies. Specifically, only a small percentage of the sample reported using eHealth for the collection of intake data (21%), patient-reported outcomes (14%), and remote patient monitoring (26%). The most common perceived barriers to implementation were cost, complexity, and unfamiliarity with available options. CONCLUSIONS: Findings provide insight into physicians' ongoing needs and barriers in providing effective pain management during the COVID-19 pandemic. Despite the potential for eHealth technologies to help address barriers in pain care, and strong interest from physicians, enhanced useability, education and training, and funding are likely required to achieve successful implementation of a broader range of eHealth technologies in the future.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".