‘Before and After’. The Journey of Patients With Low Back Pain Consulting in Elective Spine Surgery Clinics. A Qualitative Study Protocol
Bibliographic record
Abstract
INTRODUCTION: Low back pain (LBP) is a highly prevalent and disabling condition. People with LBP may be referred to elective surgical clinics for further evaluation and consideration of surgery. Despite long waits for an initial appointment, many of these patients are not surgical candidates and may be discharged, receiving minimal-to-no care, advice or alternative treatment options, leaving a critical gap in care. METHODS: Approximately 25 participants with dominant, chronic non-specific axial LBP who are (1) referred to and (2) discharged without spinal surgery following consultation in two elective spinal surgery clinics in Western Australia will participate in a one-on-one pre-consultation semi-structured interview and a similar post-consultation qualitative interview. Purposive sampling will be used to recruit participants. Interviews will be audio-recorded and transcribed, underpinned by a qualitative descriptive approach to explore participants' care journey, including pre-consultation expectations and overall experiences post-consultation. We will use inductive content analysis to analyse our data, allowing for the identification of themes that are generated from the participants' responses. DISCUSSION/CONCLUSION: This protocol outlines the methodological process for a qualitative study exploring the experiences and expectations of LBP patients before and after consulting in elective spinal surgery clinics in Western Australia. The findings may give rise to consumer-focused solutions to improve the care journey and highlight gaps in patient expectations and understanding of non-surgical management, informing the development of tailored educational resources, communication strategies and new care pathways. PATIENT OR PUBLIC CONTRIBUTION: This study will incorporate patient and/or public involvement by engaging representatives from Musculoskeletal Australia (https://muscha.org/) to contribute to the study design and interpretation of findings. Specifically, a consumer with lived experience of low back pain will be invited to review and provide feedback on the semi-structured interview questions, to ensure they are appropriate, accessible and reflective of patient experiences. CLINICAL TRIAL REGISTRATION: This study is not a clinical trial and is therefore not registered.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".