The turnover behaviour of Francophone sailors in the Canadian Navy : an example of the utility of the Canadian Forces attrition monitoring system
Bibliographic record
Abstract
The primary objectives of this study were: to confirm the structure and psychometric properties of the Canadian Forces Attrition Information Questionnaire (CFAIQ); to examine differential rates of attrition and reasons for leaving for Anglophone and Francophone sailors; and to test important components of Mobley's Expanded Turnover Process Model (1982). The CFAIQ (Lyon, 1987) captures important attrition information including reasons for leaving, CF/civilian comparisons, attitudes toward the CF experience, preparation for transition to civilian life and biographical information. Using archival data collected over a 5 year period, all Francophone and Anglophone graduates of sea-going (hard-sea) occupation training participated in the first portion of the study (N = 1077). Those who completed the Canadian Forces Attrition Information Questionnaire (CFAIQ) when leaving the Navy participated in the remainder of the study (N = 94). Consistent with previous research (e.g. Bender, Chouinard, Lee, Tanner, & Tseng, 1992; Montgomery, 1991), the results of the present study indicate Francophones continue to leave the Navy at double the rate of their Anglophone peers. In addition, Francophones reported more dissatisfaction with their CF experience and cited family issues as their most important reason for leaving as compared to job related issues for Anglophones. A LISREL analysis lent strong support to the Expanded Turnover Process Model (Mobley, 1982) upon which the CFAIQ was constructed and suggested a revised model for use in military organizations. The discussion of results includes recommendations for CFAIQ improvement and the need for a re-examination of the Realistic Job Preview (RJP) for Francophones entering the hard-sea occupations of the Navy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".