TEACHERS, CANCER, EDUCATIONAL CHANGE: WHAT “BEING ON THE BRINK OF EVERYTHING” CAN TEACH US ABOUT WELLBEING IN THE TEACHING PROFESSION
Bibliographic record
Abstract
This thesis is a deeply personal one as it amplifies the intersection between teaching and cancer, aphenomenon in education that is equally overlooked as it is common, and one that has changed my life in profound ways. Grounded in narrative inquiry research, this qualitative study explores the personal and professional lived experiences, understanding, values and meaning-making of four educators who have had a cancer diagnosis. Each of these educators have, for a time, returned to the classroom, and noticed a change in their relationship to themselves and the teaching profession as a result of their cancer journey. They have graciously shared their stories and subsequent learning with me through semi-structured interviews, artefact show-and-tell, regular collaborative dialogue, and personal reflection. Mindful engagement with field texts revealed key words, phrases, and themes which were then crafted into personal and collaborative poetic portraits using found poetry. Poetic portraits and the ensuing participant reflections have led to new personal and professional insights, meaning-making, and compelling evidence that suggests an urgent need for systemic change within the teaching profession, including prioritizing and investing in the holistic health and wellbeing of educators.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".