Integration of child life into adult oncology: A mixed‐methods feasibility study
Bibliographic record
Abstract
BACKGROUND: Cancer in a loved one can have negative effects on child health and development. Child Life Specialists (CLSs) specialize in assisting children understand and cope with difficult medical scenarios but are generally not available in adult care facilities to support the needs of patient-families with minor children. We conducted a mixed-methods study of the implementation of a pilot CLS program at a tertiary oncology centre. METHODS: We collected administrative and clinical data on referred families; encounter data; and patient-reported questionnaire data before and 2 months after engagement with the program. RESULTS: Over the initial 10 months, 98 families were referred, 91 of whom engaged through a total of 257 clinical encounters. The cancer patient in the family was most commonly a woman with a mean age of 45 years and in the role of mother. Breast cancer was the most common diagnosis (24%) and 78% of patients had stage IV disease. Most families had >1 child at home, and children were most commonly school-aged (5-14y). Phone and Hospital/Clinic visits accounted for the largest portion of CLS time. Interventions ranged from diagnosis education through to bereavement support. Most cancer patients indicated that the program was helpful to them and their families. There were trends of moderate improvements on patient reported outcomes. CONCLUSION: Our study was able to provide an understanding of the initial CLS program operations to guide program development and future study. Such a program holds promise as an important aspect of adult oncology family-centered 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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".