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A Comparative Physical Demands Analysis of the Canadian Navy, Army and Air Force

2010· article· en· W2093826583 on OpenAlexaffabout
Michaël Spivock, Tara Reilly, Rachel E. Blacklock, Lindsay Goulet

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsNavyAeronauticsContext (archaeology)SprintTask (project management)Operations researchMilitary personnelEngineeringComputer scienceSimulationSystems engineeringGeography

Abstract

fetched live from OpenAlex

PURPOSE: In the context of the Canadian Forces Health and Fitness Strategy and as a preliminary step in the development of fitness standards which take into account the specific demands of the Navy, Army and Air force, detailed task analyses were conducted in each of these environments. The objective of this phase of research was to identify common, critical, physically demanding tasks in each of the environments. METHODS: Data collection lasted approx 18 months and methods included (1) task analysis surveys; (2) on-site measurements of loads, distances, and heights; (3) focus groups and interviews with subject matter experts; (4) job shadowing; (5) heart rate monitoring during tasks; and (6) narrative descriptions of daily tasks, military exercises and operations. Subject matter experts, the military chain of command and project management team members were called on to reconcile the information yielded by the various methods within each environment in order to obtain consensus on the nature of the common, critical and physically demanding tasks. RESULTS: Tasks were eventually grouped into the categories of moving, carrying, pulling & dragging, lifting, climbing and special TASKS. Though all environments performed these general categories of tasks, the weights, distances, equipment, obstacles, time requirements and frequencies varied greatly between Navy, Army and Air force. In the category of MOVING for example, Navy personnel were called to walk 50m in 20kg bunker gear, Army personnel reported marching 5km with 30kg loads whereas Air force personnel could be called to sprint 500-750m in the case of a downed aircraft. Similar patterns of variation between environments were identified for carrying, pulling & dragging, lifting, climbing and special tasks. CONCLUSIONS: Though all Canadian Forces Personnel need to be fit in order to perform universal tasks (e.g., various forms of casualty evacuations, assistance to civil powers in natural disasters) there remain demands specific to each environment. These environment-specific requirements need to be taken into account in the development of Forces-wide fitness standards and programs to ensure that all personnel are operationally fit and effective.

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)0.002
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 score0.432
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.419
Teacher spread0.382 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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