MétaCan
Menu
Back to cohort
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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.112
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 teacher head, 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

Explore more

Same venueWorkSame topicOccupational Health and PerformanceFrench-language works237,207