A study of Manitoba principals' experiences of workplace mistreatment, its frequency, its severity and its impacts
Bibliographic record
Abstract
In Canada and in many jurisdictions, the job of principals has been described as complex (Combs, Edmonson & Jackson, 2009; Duke, 1988; Pollock & Ryan, 2013). With such a task, levels of stress and job dissatisfaction could affect the ability of principals to fulfill all job requirements (Keashly, 1997; Raver & Nishii, 2010). One such stressor on a principal could be elements associated with mistreatment by other adults in the principals’ workplace network. There are no studies in the academic research to date that touch specifically on the types of general workplace mistreatment (Price Spratlen, 1995) that the principals suffer or the impacts that these incidents have on the principals. The purpose of this research was to contribute to the filling of this void by examining what twelve middle years school principals in Manitoba perceived as mistreatment from adults, the attributes of the mistreatment incidents regarding alleged perpetrators, frequency and severity of the incidents and the impacts on principals, particularly regarding well-being and job satisfaction. This exploratory study used mixed methods inquiry (Creswell & Plano Clark, 2011) focusing on uncovering both quantitative data regarding the principals’ perception of the frequency and severity of the mistreatments, as well as qualitative data regarding the ways that principals constructed understandings of mistreatment and its impact. A heuristic framework was used to catalogue the perceptions of workplace mistreatment provided by the principals using classifications drawn from the organizational behaviour literature, Namie and Namie’s (2004) Workplace Mistreatment Severity Continuum and Blase and Blase’s (2006) Levels of Aggression for Workplace Mistreatment. Findings from this study suggested, first, that the principals participating in this study did experience incidents of general workplace mistreatment ranging from incivility to mobbing, but not physical violence. Secondly, the survey results revealed that the frequency of mistreatments was not high, but that when the incidents did occur, some were perceived as severe and stressful regardless of where they fell on the workplace mistreatment continuum heuristic. Principals suffered a range of negative impacts, some enduring, such as stress, but the negative impacts did not prevent them from expressing high levels of overall job satisfaction.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".