Why corrections? Motivations for becoming a Canadian provincial or territorial correctional officer
Bibliographic record
Abstract
Purpose The growing prison population and challenges related to recruiting and retaining suitable correctional officers (COs) in Canada provided the impetus for the current study. Recruitment efforts in provinces and territories often rely on a variety of diverse testing, ranging from physical fitness to in-person interviews. However, despite such efforts, turn-over rates remain high and insight into what motivates people to seek a career in correctional work continues to require elucidation. By investigating the career development of COs, the purpose of this paper is to understand why certain men seek employment in this field. Design/methodology/approach Data are derived from in-depth semi-structured interviews with male officers (n=41) who have employment experience in provincial correctional facilities. Findings Respondents discussed their initial motivations for entering the field and their career ambitions. Some entered corrections because they lacked alternative options for employment, others as a stepping stone for a different justice-oriented occupation such as policing or parole. Many were motivated by the income and benefits or had a specific vocational self-identity toward correctional work. However, nearly all respondents indicated that, over time, they no longer felt their rate of pay justly reflected the demands of the occupation, thus factors motivating field entry fail to materialize in ways that sustain long-term employee retention. Practical implications CO recruitment should target individuals with interest in the field of justice more broadly. Occupational demands of the CO occupation need to be addressed to reduce turnover. Recruitment should focus not only on new graduates but also be directed toward more mature individuals. The impact of resource intensive interview processes for candidates on turnover rates needs to be evaluated. Originality/value The authors’ focus on COs with employment experience in provincial and territorial correctional facilities, rather than federal, is justified by the lack of research on and the high rate of CO turnover in such facilities. After exploring the qualitative responses of officers, clear themes emerged that align well with natural socio-ecological systems: the self, family and community, and wider society.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".