Clinicians’ Perspective on Implementing Virtual Hospital Care for Low Back Pain: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Alternate "hospital avoidance" models of care are required to manage the increasing demand for acute inpatient beds. There is currently a knowledge gap regarding the perspectives of hospital clinicians on barriers and facilitators to a transition to virtual care for low back pain. We plan to implement a virtual hospital model of care called "Back@Home" and use qualitative interviews with stakeholders to develop and refine the model. OBJECTIVE: We aim to explore clinicians' perspectives on a virtual hospital model of care for back pain (Back@Home) and identify barriers to and enablers of successful implementation of this model of care. METHODS: We conducted semistructured interviews with 19 purposively sampled clinicians involved in the delivery of acute back pain care at 3 metropolitan hospitals. Interview data were analyzed using the Theoretical Domains Framework. RESULTS: A total of 10 Theoretical Domains Framework domains were identified as important in understanding barriers and enablers to implementing virtual hospital care for musculoskeletal back pain. Key barriers to virtual hospital care included patient access to videoconferencing and reliable internet, language barriers, and difficulty building rapport. Barriers to avoiding admission included patient expectations, social isolation, comorbidities, and medicolegal concerns. Conversely, enablers of implementing a virtual hospital model of care included increased health care resource efficiency, clinician familiarity with telehealth, as well as a perceived reduction in overmedicalization and infection risk. CONCLUSIONS: The successful implementation of Back@Home relies on key stakeholder buy-in. Addressing barriers to implementation and building on enablers is crucial to clinicians' adoption of this model of care. Based on clinicians' input, the Back@Home model of care will incorporate the loan of internet-enabled devices, health care interpreters, and written resources translated into community languages to facilitate more equitable access to care for marginalized groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".