ADVANCING INTERPROFESSIONAL PRIMARY HEALTH CARE SERVICES IN RURAL SETTINGS FOR PEOPLE WITH CHRONIC LOW BACK DISORDERS: A TEAM AND TECHNOLOGY APPROACH
Bibliographic record
Abstract
Background Rural Canadians are more likely to have chronic back disorders than their urban counterparts. Their barriers to accessing providers with expertise in chronic back disorder management include: reduced availability of local practitioners and lengthy travel requirements. Joining an urban Physical Therapist (PT) with expertise in chronic back disorders with a rural primary team and patient using telehealth may be an option for this disparity in access.\nMethods This dissertation includes three studies presented in the following manuscripts: 1) A systematic review examining the use of videoconferencing by PTs for the management of musculoskeletal conditions; 2) A comparison of three different intervention groups: PTalone, Nurse Practitioner alone (NPalone), and NP/PTteam to determine the agreement of the models of care on diagnosis and management decisions; and 3) an examination of the experiences of patients and practitioners involved in a teamand technology model of care for chronic back disorders.\nResults Gaps in the literature included: few large RCTs and comparative studies, an absence of studies examining interprofessional models of care, no examination of combined telehealth and in-person types of care, and the need for more rigorous study designs to facilitate meta-analysis. The NP/PTteam made similar decisions regarding diagnosis and management for chronic back disorders compared to an in-person PT. This demonstrated that the contribution of PT to the team resulted in the same findings as a PT who examined a patient independently. It is a feasible method of managing chronic back disorders in rural areas, and is met with satisfaction by patients and practitioners.\nAnalysis of semi-structured interviews of patients and practitioners who experienced the team and technology model of care identified the following themes: access to care for chronic back disorders, effective interprofessional practice (team), enhanced clinical care for CBD, and technology.\nConclusions A team and technology approach to care is comparable to in-person PT for diagnosis and management decisions in chronic back disorders. This approach can enhance access to care for chronic back disorders in rural areas and result in improved clinical care for rural residents with chronic back disorders.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".