Recommendations for Handling Stress in the Health Care and Education Environment: An Interview with
Bibliographic record
Abstract
Catherine Kiteley is a registered nurse holding a Masters of Science degree from the University of Toronto and certifications in Oncology CON(C) and Palliative care CHPCN(c), She currently works as a Clinical Nurse Specialist in supportive care with a focus in oncology and palliative care at The Credit Valley Hospital, the Peel Regional Cancer Centre . Catherin e has been a nurse professional for 30 years and has held a variety of nursing positions including, staff nurse, nurse educator, nursing unit administrator and program director. She derives much job satisfaction in her current role as an advanced practice nurse as it enables her participation in not only clinical practice, but education, research and organizational leadership. In addition to working at the hospital, Catherine holds a clinical appointment with the University of Toronto where she teaches and provides mentorship for graduate nursing students. She is also published in a number of scholarly journals and speaks at regional, national and international conferences on various topic areas related to cancer nursing.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.016 |
| 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".