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Record W6999721459

Developing a Pre-Surgical Rehabilitation Intervention for Individuals with Symptomatic Lumbar Spinal Stenosis

2024· dissertation· en· W6999721459 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typedissertation
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
FundersAlberta InnovatesUniversity of AlbertaCanadian Institutes of Health ResearchPhysiotherapy Foundation of CanadaMcMaster UniversityWorkSafeBCCanadian Orthopaedic FoundationArthritis Society
KeywordsPrehabilitationRehabilitationIntervention (counseling)Qualitative researchPerioperativeRandomized controlled trialLumbar spinal stenosisReferralHealth care
DOInot available

Abstract

fetched live from OpenAlex

Symptomatic lumbar spinal stenosis (SLSS) is a common cause of pain and disability among older adults and is the most common indication for lumbar spine surgery in older adults. However, one-third of patients do not experience clinically important improvements in pain or disability after surgery. This thesis included 4 studies with an overarching aim of understanding the perioperative experience of SLSS patients and developing a patient-centred prehabilitation program to improve post-operative outcomes. The first study aimed to understand SLSS patient’s surgical decision-making. We conducted qualitative interviews (n=35) and used inductive phenomenology to identify five themes: previous experiences with non-surgical management, worrisome symptoms impacting functionality, perception of surgery as a final course of action, post-surgical hopes/expectations, and having a social support network. The second study aimed to understand the unmet needs and expectations for the management of SLSS among the same participant group. We identified four themes: the complexity of navigating the healthcare system, the need for strengthened perioperative management, the absence of peer support as a desired opportunity, and difficulty navigating post-operative recovery without healthcare provider support. The insights from the qualitative studies informed the development of an 8-week, patient-centered prehabilitation intervention, which included principles of cognitive behavioural therapy, motivational interviewing, graded activity, education and peer support. A feasibility randomized controlled trial (RCT) comparing the prehabilitation to minimal intervention was conducted, including outcomes of recruitment rate, adherence, program satisfaction, attrition, and barriers and facilitators. There was a 57% referral acceptance rate and challenges with recurrent and attrition due to early surgery, meaning that pilot feasibility criteria were not met. However, participants that completed the intervention reported high satisfaction with care, although this should be interpreted with caution given the small sample size. Parallel to the RCT, we conducted a longitudinal qualitative study (semi-structured interviews before and after the intervention and 3 months after surgery). We identified four recovery trajectories among participants: sedentary struggle, dynamic recovery, dynamic struggle, and dynamic resilience. These trajectories highlighted the role of changing attitudes and behaviours towards exercise and physical activity in recovery. The findings of this thesis suggest the need for targeted management pathways for SLSS, emphasizing the importance of integrating a psychosocial approach.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.016
GPT teacher head0.281
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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