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Record W4415488453 · doi:10.1515/sjpain-2025-0003

Understanding the experiences of Canadian military veterans participating in aquatic exercise for musculoskeletal pain

2025· article· en· W4415488453 on OpenAlexafffundabout
Nicholas Held, Jordan D. Miller, Heidi Cramm

Bibliographic record

VenueScandinavian Journal of Pain · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsQueen's UniversitySt. Joseph’s Healthcare Hamilton
FundersQueen's University
KeywordsRehabilitationMusculoskeletal painMEDLINEMusculoskeletal disorderMilitary personnel

Abstract

fetched live from OpenAlex

OBJECTIVES: Canadian Armed Forces (CAF) Veterans report living with chronic pain at nearly double the rate of comparable Canadians. They have unique pain management needs, highlighting the need for further research. Aquatic exercise is one treatment option that may offer meaningful benefits. This study aimed to explore the lived experiences of CAF Veterans incorporating aquatic exercise as part of their pain management routines, offering insight into its potential role in managing chronic pain among military Veterans. METHODS: Informed by interpretive phenomenological analysis, ten CAF Veterans with chronic pain and experience with aquatic exercise were interviewed. RESULTS: Three superordinate themes were constructed: Respite from the pain; reconstructing an identity as a physically capable individual; and reclaiming a sense of self and seeing a new path forward. Exercise has the potential to improve pain, function, and quality of life for individuals living with pain. Among CAF Veterans, aquatic exercise can further support personal growth, development, and recovery after release from the military. CONCLUSIONS: The findings provide insight for rehabilitation providers of the positive experiences and additional impacts of participating in aquatic exercise for CAF Veterans with chronic pain. Rehabilitation providers may consider incorporating aquatic exercise into physical activity-based rehabilitation programs for CAF Veterans living with musculoskeletal pain. The current investigation was approved by and followed the recommendations of the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board at Queen's University (REH-797-21).

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.314
Teacher spread0.272 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2025
Admission routes3
Has abstractyes

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