A qualitative exploration of unmet healthcare needs for individuals undergoing surgery for symptomatic lumbar spinal stenosis
Bibliographic record
Abstract
PURPOSE: This study aimed to gain an in-depth perspective of the perioperative experiences, including unmet needs and expectations, of individuals with the Canadian healthcare system before or after surgery for symptomatic lumbar spinal stenosis (SLSS). METHODS: We used qualitative interpretive phenomenology to study individuals with SLSS. We conducted semi-structured qualitative interviews that lasted 30 to 90 min. Inclusion criteria were individuals 55 or older, diagnosed with SLSS, scheduled for or undergone lumbar spine surgery, and able to speak English. RESULTS: = 14) were included in this study. Among those participants, 15 were interviewed preoperatively (before surgery) and 17 postoperatively (after surgery). Patients described varied perioperative challenges, requiring a tailored approach to meet their unique needs. We constructed 4 major themes that participants highlighted as factors affecting their perioperative experience: (1) Frustration and barriers to navigating the healthcare system, (2) Insufficient education and preparation for surgery, (3) Challenges with postoperative recovery and rehabilitation, and (4) Unmet needs for peer and emotional support. CONCLUSION: This study highlights the importance of developing patient-centered perioperative standard of care to help individuals undergoing surgery for SLSS navigate the Ontario healthcare system.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".