The Career-Life Development of Teachers Post-Assault by Students: An Interpretative Phenomenological Analysis
Bibliographic record
Abstract
Career-life development is a long-term process that requires people to adapt to changes in their life roles and vocational-social environment. Teachers often must be flexible to meet school and student demands. The constant need to be adaptive can be taxing and result in teachers exiting the profession early in their careers. One factor that contributes to teachers leaving the profession is student violence. Given that student violence toward teachers is a problem, the purpose of this Interpretative Phenomenological Analysis (IPA) thesis is to explore how being assaulted by a student impacts the career-life development of veteran secondary school teachers (i.e., at least 10 years in the teaching profession) in Western Canada. A total of seven participants completed two semi-structured qualitative interviews via Zoom. Participants’ interview responses were transcribed verbatim, and all participants verified that the transcription of their interviews was accurate. Data analysis involved coding each participant’s interviews which produced 14 Group Experiential Themes (i.e., the common shared and unique themes of participants’ lived experiences). The Group Experiential Themes were organized thematically as being about society, the educational system, and the individual. Common themes at the individual level highlighted the significance of self- and teacher-compassion and purpose connected to maintaining school community bonds in participants’ recovery process and continuation in the profession. The thesis provides recommendations targeted at improving teacher wellbeing post-assault. The limitations of this study and future directions for research are discussed as well.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".