An Advanced Practice Physiotherapy Spine Triage Service for Adults with Neck and Back Pain: A Feasibility Study
Bibliographic record
Abstract
Background Adults with degenerative spine disease constitute a significant proportion of referrals to Ontario neurosurgeons. By 2024, the demand is projected to increase by 67%. Wait times for patients to be seen by a spine surgeon in southeastern Ontario are the longest in the province. Purpose of Study A three-phase intervention study was designed to determine whether an Advanced Practice Physiotherapist (APP) Spine Triage model was feasible within the context of the Neurosurgical Referral Clinic. Methods Sample. In Phase I and III, three categories of participants were sampled using a consecutive sampling technique: patients scheduled for a spine assessment; healthcare providers who referred patients to the Neurosurgical Referral/APP Spine Triage Clinic; and, Neurosurgical Referral/APP Spine Triage Clinic and hospital stakeholders. In Phase II, key stakeholders were recruited to participate in the planning and development of the new APP role using a purposive sampling technique. Procedure. Phase I assessed the current Neurosurgical Referral practice using a descriptive observational cross-sectional design. Phase II used a modified case study design to determine the satisfaction with the APP role development process amongst stakeholders while Phase III utilized a cross-sectional multiple methods study design to determine the acceptability and feasibility of this new role. Instruments. Various quantitative and qualitative tools were used to collect data related to demographic and symptom characteristics, self-perceived pain, function, quality of life, wait times, and stakeholder satisfaction. Results The APP Spine Triage Service for adults with neck and back pain was feasible within the context of the Neurosurgical practice with higher rates of stakeholder satisfaction and very strong support for the new model of care. The findings in the current study are in line with existing literature that the APP model provides access to patient-centred care that is timely, informative, coordinated and integrated to better meet the health needs of patients with neck and back pain. Implications The APP model of care will provide early identification of patients for whom non-surgical management has been deemed the most appropriate approach along with education, reassurance, and evidence-based self-management strategies that will be linked to local programs and resources.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".