“There’s So Much More Support We Could Have Provided”: Child Life Specialists’ Stories of the Challenges Working in Adult Oncology
Bibliographic record
Abstract
A cancer diagnosis in patients who are parents of minor children is uniquely stressful for both parents and children. Children need developmentally appropriate information and support to help reduce their fears and worries. Child life specialists (CLSs) are health professionals who work in pediatric environments to support children and families with the stress and uncertainty of illnesses. Increasingly, CLSs have been called upon to support children of patients in adult clinical environments. Our objective was to elucidate CLS caregiving narratives related to working with children of adult cancer patients. We used narrative inquiry to interview four CLSs working in adult oncology. Canadian CLSs who have experience providing care for children and families affected by parental cancer were recruited via convenience sampling. We used narrative analysis methods that included multiple close reads of the data, generating narrative themes, and noting conflicts or tensions in the data. CLSs' caregiving stories often highlighted the complexities of working in an adult oncology environment. Their narratives included challenges in providing optimal care to the children, including family-level barriers (such as parental wishes to withhold information from their children) and systemic barriers (such as late referrals and limited options for bereavement support). CLS participants identified several challenges of working with families in adult oncology. The CLSs highlighted a desire for additional institutional support for children of adult oncology patients and for themselves working in these environments in order to achieve what they believed to be optimal care.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".