Patients and Advanced Practice Physiotherapists’ Experiences and Perceptions in a Specialized Spine Model of Care: A Qualitative Study
Bibliographic record
Abstract
Purpose: Models of care using advanced practice physiotherapists (APPTs) have been proposed as a solution to improve health care access and efficiency for patients with spinal disorders referred to specialized spine surgery care. The objective was to explore the experiences and perceptions of both APPTs and patients with spinal disorders within a specialized spinal surgery model of care. Method: APPTs and patients with spinal disorders who provided or received care as part of a randomized controlled trial evaluating a new spinal surgery model of care were invited to participate in this qualitative study. Semi-structured interviews were performed to identify themes related to their experiences and perceptions of this model of care. Verbatim transcripts were coded and analyzed using an inductive thematic analysis. Results: Seven APPTs and nine patients were included. Three main themes emerged from our analysis: (1) The standard medical model of care for spinal disorders is problematic; (2) the advanced practice physiotherapy model of care is a great solution to help manage patients with spinal disorders; but (3) there are areas for improvement in this model. Conclusions: Overall, patients and APPTs were positive regarding this advanced practice physiotherapy model of care as a solution to improve health care access and quality.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".