Good bye burnout, hello me: individual strategies of self-care among Saskatchewan teachers
Bibliographic record
Abstract
Teachers with less than five years teaching experience have a high attrition rate. This high rate has financial, organizational and instructional consequences, such as school divisions that must recruit and train replacements, and students who lose the value of being taught by teachers who have gained experience in the profession. Self-care is a factor that curbs attrition, however, little is known about the personal and professional strategies of self-care for teachers. The Delphi method was used to identify and understand the self-care strategies used by Saskatchewan schoolteachers. Fourteen participants with five or more years of teaching experience and from nine different school divisions in Saskatchewan contributed to the study. Each participant responded through two rounds of online questionnaires about his or her self-care practices. Self-care is associated with well-being and it is the individual teacher that can take steps to cultivate and maintain personal health. Data were analyzed using SurveyMonkey and NVIVo 9 qualitative analysis software programs, and Skovholt’s theoretical model of self; strategies and themes were identified. A visual representation of participant’s responses was developed. The most common self-care strategies identified were talking with friends and family, healthy eating, discussing events from the classroom with support system at school, drinking water, and volunteering. The findings are described alongside implications for teachers and other helping professionals as well as future research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".