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Record W1681983466 · doi:10.3233/wor-2008-00712

Physical training of combat diving candidates: Implications for the prevention of musculoskeletal injuries

2008· article· en· W1681983466 on OpenAlexaffabout
Thomas W. Pelham, Laurence E. Holt, Harold C. White

Bibliographic record

VenueWork · 2008
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTraining (meteorology)Physical therapyMedicinePhysical medicine and rehabilitationMusculoskeletal injuryAeronauticsEngineeringAlternative medicine

Abstract

fetched live from OpenAlex

Physical training (PT) is a significant component in the operational preparation for Canadian Forces (CF) combat divers. However, in developing the various components of a PT program, consideration must be given to the potential for increasing the risk of injury. Indeed, all PT programs should incorporate components that minimize risks of injury, thereby attempting to prevent injury. This report identifies high-risk activities associated with PT during a CF Combat Diving Course and contains recommendations for PT modifications. Major high-risk activities identified were: inappropriate exercises, errors in exercise prescription, particularly in intensity and duration and incorrect lifting methods. From a specificity of training perspective, there is little support for the incorporation of 'dry land flutter kick' (i.e., repetitive, unsupported alternating straight leg raises), or high repetitions of push-ups, sit-ups and chin-ups in the training of CF combat diving candidates. Excessive use of these exercises, as performed during training, pose a high-risk for injury and are not recommended.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.112
GPT teacher head0.468
Teacher spread0.357 · 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 designObservational
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

Citations5
Published2008
Admission routes2
Has abstractyes

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