“It Could Have Been Me”: An Interpretive Phenomenological Analysis of Health Care Providers' Experiences Caring for Adolescents and Young Adults with Terminal Cancer
Bibliographic record
Abstract
Purpose: Adolescents and young adults (AYAs) with terminal cancer are a marginalized population with unique medical and psychosocial needs. AYAs commonly report challenges with their health care experiences, however, little is known about the experiences of the health care providers (HCPs) who deliver this specialized care. The purpose of the current study was to understand HCPs' experiences caring for AYAs with terminal cancer. Methods: Nine HCPs (four nurses and five physicians) took part in in-depth semistructured interviews. Participants were eligible if they were a nurse or physician in Atlantic Canada; cared for at least one AYA patient with terminal cancer in the past 3 years; and were able to speak and understand English. Data were analyzed using interpretive phenomenological analysis. Results: Analyses revealed four superordinate themes present in the data: (1) many unknowns and uncertainties associated with providing care for AYAs compounded by minimal or no training specifically concerning this population; (2) an intense emotional experience compared with caring for patients with terminal cancer of other ages; (3) personal identification with patients and their families; and (4) attempts to make sense of the circumstance thwarted by feelings of injustice and unfairness. Conclusions: HCPs experienced unique emotional and logistical challenges when caring for AYAs with terminal cancer, which can influence the care they provide. HCPs' experiences highlight the need for training to support clinicians in caring for AYAs with terminal cancer to optimize their own well-being and delivery of health care services to this population.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".