Measuring what gets done: Using goal attainment scaling in a vocational counseling program for survivors of childhood cancer
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors face education and employment challenges due to physical, cognitive, and psychosocial effects of the disease and treatments, with few established programs to assist them. The objectives of this study were to describe the implementation of Goal Attainment Scaling (GAS) to evaluate an educational and vocational counseling program established for survivors of childhood cancer, and analyze patterns of program engagement and client outcomes, stratified by demographic and diagnostic characteristics. METHODS: A population-based retrospective cohort study of childhood cancer survivors who were engaged with the Pediatric Oncology Group of Ontario's School and Work Transitions Program (SWTP) between January 2015 and December 2018 was utilized. Survivors were followed from SWTP engagement until May 30, 2019 to capture goal attainment. Individual goals were summarized across various demographic, disease, and treatment strata. RESULTS: In total, 470 childhood cancer survivors (median age = 17.9, 58% male) set 4,208 goals in the SWTP during the study period. The mean length of observation was 130.8 weeks (SD = 56.9). Overall, 68% of the goals were achieved. Eighty-three percent of the goals related to further education. Clients diagnosed with a solid tumor set the most goals on average, followed by those with central nervous system tumors and leukemia/lymphoma. CONCLUSIONS: The SWTP assists childhood cancer survivors in realizing their academic and vocational goals. Application of GAS in this setting is a feasible way to evaluate program outcomes. From the volume and breadth of the GAS goals set and achieved, the overall success of the SWTP appears strong.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".