Connecting Calgary teachers with resources to improve and alleviate burnout
Bibliographic record
Abstract
Between fostering relationships with parents, students, and colleagues and navigating a neverending list of task to complete in a given day, it is no question that teachers are experiencing high levels of stress. As a result, more and more teachers are opting to leave the education field, not because they lack the passion for their job, but because they feel that they are no longer able to cope with the stress. Research has demonstrated that, although teachers are attempting to cope with an increased workload with minimal resources, they are struggling with ways to ensure that they are taking care of their own well-being. The research suggested that more supports need to be in place to support teachers’ well-being while they are experiencing burnout. In this vein, I designed a website with the aim of supporting teachers while they are experiencing burnout and educate them on the resources available to them within the Calgary area through both the CBE and community resources. Finally, this website offers a resource collection of videos, articles, books, and Web resources that teachers can access to support and motivate them to stay in the profession. I hope that #TeacherWellnessYYC will be an information guide for all teachers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".