Development and Implementation of Evidence-Based Physical Employment Standards
Bibliographic record
Abstract
Reilly, TJ, Gebhardt, DL, Billing, DC, Greeves, JP, and Sharp, MA. Development and implementation of evidence-based physical employment standards: key challenges in the military context. J Strength Cond Res 29(11S): S28–S33, 2015—The use of evidence-based physical employment standards is critical in selecting individuals who can meet the requirements of arduous military occupations. The methods used to generate the physical assessments and standards are critical to the process and must withstand legal scrutiny. This article addresses the challenges encountered when developing, validating, and implementing physical standards and assessments. The challenges covered by the study include: (a) identification of critical job tasks and minimum requirements for performance of the tasks, (b) involvement of military personnel as subject-matter experts, (c) development of tests and criterion measures linked to critical job tasks, (d) determination of test performance standards, (e) evaluation of bias for protected groups, and (f) implementation, development of test policies, and revision of tests and standards.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.484 | 0.536 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".