EXPLORING TEACHER RECRUITMENT AND RETENTION POLICIES, PRACTICES, AND PERSPECTIVES IN SASKATCHEWAN AND WEST VIRGINIA A DESCRIPTIVE STUDY
Bibliographic record
Abstract
This descriptive study explored and compared what recruitment and retention policies and practices school divisions in Saskatchewan, Canada and West Virginia, United States used to staff their schools and whether or not, and to what degree, institutional isomorphic forces influenced those policies and practices. Survey data from 21 Saskatchewan and 19 West Virginia school divisions and semi-structured interview data from five self-identified survey respondents was collected. Descriptive and inferential statistical techniques and simple qualitative descriptive techniques were used to analyze the data.\nThis study corroborated the suggestion in the literature that school divisions have similar staffing administrative functions, regardless of their location in Saskatchewan or West Virginia; and further that the policies and practices used to recruit and retain teachers are comparable. By extension, school divisions generally have administrative functions, such as staffing, that may be described and compared aculturally. The data supports this assertion. Further, the study also found that institutional isomorphic (coercive, mimetic, and normative) forces affected policies and practices related to recruitment and retention of teachers. All respondents indicated at least some effect on policies and practices by each isomorphic force. The data showed the effect of coercive influencing forces indicates that the Saskatchewan school divisions have a greater degree of autonomy and self-governance than their counterparts in West Virginia. \nFurther, significant findings from the survey data in this study indicated recruiting and retention in rural/remote school divisions was more difficult than non-rural/remote school divisions; recruiting and retention were more difficult for school divisions in West Virginia than school divisions in Saskatchewan. Additionally, the data indicated recruiting and retention challenges and issues in West Virginia were more often related to financial factors compared to Saskatchewan school divisions where non-financial factors were more of an issue.\nAnalysis indicated statistically significant differences between West Virginia and Saskatchewan school divisions around challenges they face when recruiting teachers. These challenges included low/uncompetitive salaries (p=.000), certification requirements (p=.000), degree requirements (p=.002), and uncompetitive benefits (p=.002). Likewise, statistically significant differences between West Virginia and Saskatchewan were also revealed regarding the challenges school divisions face when retaining teachers. The challenges that affected teacher retention were low/uncompetitive salaries, certification requirements, degree requirements, closer proximity to higher paying divisions, and uncompetitive benefits. West Virginia had much more difficulty and faced greater barriers in recruiting and retaining teachers than Saskatchewan. When looking at specific recruitment and retention strategies, West Virginia schools were more likely to rely on assisting teachers in obtaining full license/certification as a strategy than divisions in Saskatchewan, which is likely related to the use of alternative certification practices and provisionally licensed staff in West Virginia.\nThis study illustrated recruitment and retention policies and practices overall are similar among school divisions in Saskatchewan and West Virginia. However, although both school divisions face similar challenges, the West Virginia school divisions were affected more by financial factors, while the Saskatchewan school divisions were affected more by non-financial factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".