The Alberta Back Care Pathway: The feasibility of implementing a novel care pathway to improve low back pain management for family physicians in primary care
Bibliographic record
Abstract
BACKGROUND: Family physicians in Canada's universal healthcare system often manage low back pain patients using interventions not recommended in clinical guidelines, such as pharmaceuticals, imaging and spinal injections, while guideline-based treatments like education and exercise remain unfunded. The Alberta Back Care pathway was developed to address this gap, offering funded, evidence-based care for low back pain patients in 5 streams (acute, sub-acute, chronic, chronic non-responsive and stable radiculopathy). OBJECTIVE: To evaluate the feasibility of implementing the pathway in two urban Primary Care Networks in Alberta, Canada. MATERIALS AND METHODS: Each of the 5 pathway streams provided physicians with information scripts, no-cost interventions (pharmaceuticals and otherwise) and interventions to avoid. From April 2021 to November 2023, the RE-AIM framework was used to assess implementation feasibility of the pathway. RESULTS: For the RE-AIM dimension of reach, 25% (n = 41/162) of eligible family physicians in Primary Care Network "A" and 12% (n = 26/221) in Primary Care Network "B" enrolled in the study. Over half of enrolled physicians (n = 21/41 and 21/26) referred at least one patient with most referrals to the GLA:D Back program for chronic low back pain stream (93% in network "A" and 88% in network "B"). Implementation, evaluated as the proportion of referrals by physician compared to their total low back pain caseload, was low (> 0-10% referred) for 52% (n = 11/21) of physicians in network "A", and medium-low (10-25% referred) for 52% (n = 11/21) of physicians in network "B". The number of pathway-appropriate patients in each physician's caseload was unknown. Maintenance at 12 months was 56% (n = 10/18) in network "A" and 39% (n = 7/18) in network "B". CONCLUSION: The Alberta Back Care pathway was feasible to implement during the pandemic and primarily serving patients with chronic low back pain by providing access to a guideline-based education and exercise group program (GLA:D Back).
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".