A qualitative examination of the benefits and challenges of a psychosocial screening intervention in pediatric oncology: “Support comes to us”
Bibliographic record
Abstract
BACKGROUND: Pediatric cancer diagnosis and treatment can have detrimental mental health effects on parents (caregivers) and their children/adolescents (youth). Psychosocial screening and intervention have been recognized as standards of care in pediatric oncology. The most effective psychosocial interventions to support those in need post screening have not been determined. AIMS: This qualitative study aimed to investigate the perceived benefits and challenges for caregiver and youth participants in the screening-intervention arm of an Enhanced Psychosocial Screening Intervention (EPSI) pilot study. METHODS: EPSI consists of a psychosocial navigator (PSN) who shares screening results conducted near diagnosis (T1) and monthly for 1 year (T2) with treating teams and families. All 17 caregiver-youth dyads who had completed EPSI were invited to participate in a semi-structured interview. RESULTS: Ten caregivers and nine youth participated. Identified themes were grouped into benefits and challenges of EPSI: feeling supported and cared for (support comes to us regularly, having someone to talk to); and feeling empowered through knowledge of resources and services were perceived as benefits. Caregivers were challenged by feeling overwhelmed, and youth by screening questions perceived as too repetitive. CONCLUSIONS: Regular monthly contacts for a year by the PSN with screening results and recommendations were perceived as beneficial by youth newly diagnosed with cancer and their caregivers who participated in EPSI. Feeling that support came to them and they had someone to talk to was a critical component. While information about psychosocial resources was not always used right away, it did evoke feelings of being empowered.
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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".