Falling through the cracks. A thematic evaluation of unmet needs of adult survivors of childhood cancers
Bibliographic record
Abstract
OBJECTIVE: The population of adult survivors of childhood cancers (ASCCs) is growing, resulting in unique long-term challenges. This study explored experiences of perceived unmet ASCC survivorship needs. METHODS: We invited ASCCs to complete surveys sent through the cancer registry. Four open-ended questions allowed participants to write in comments. We analyzed responses to these open-ended questions thematically, employing a process of constant comparison. RESULTS: Our sample included 94 ASCCs who completed open-ended questions (61 female; aged 20-78 years, mean age = 34.47, SD = 11.84, mean = 23.27 years post diagnosis). Identified themes included (1) overlooked experiences of distress; (2) lack of counseling: system, patient, and family barriers; (3) difficulty negotiating future life milestones exacerbated by lack of knowledge; and (4) dissatisfaction with service provision: past and present. Prevalent issues identified by participants included lack of supportive care to address needs, distress due to missed developmental milestones as a result of cancer, lack of knowledge about late-term and long-term effects of cancer treatment, and concern over absence of organized long-term follow-up. CONCLUSIONS: Adult survivors of childhood cancers continue to experience unmet needs during their cancer diagnosis, treatment, and long into survivorship due to the treatment for cancer and ongoing side effects. Solutions could focus on addressing the needs of survivors to bridge system gaps and barriers. Specifically, there is a need to improve psychological interventions and transitions from pediatric to adult-care facilities.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".