The Transferability of Goal Attainment Scaling (GAS) for Child Life Specialists Working in Pediatric Rehabilitation: A Critical Review of the Literature
Bibliographic record
Abstract
Objective: There is much variation in the way child life specialists implement and document their interventions and services, especially among practice arenas. This variation includes the methods child life specialists use to set individual goals with pediatric clients and families, as well as to evaluate the effectiveness of their interventions and services. The purpose of this paper is to highlight how goal attainment scaling (GAS) could and should be inte-grated into the daily practices of child life specialists working in a pediatric rehabilitation setting. Method: GAS is a widely used individualized outcome measure, designed to assess whether individuals have achieved the goals of intervention by quantifying their progress (Kiresuk & Sherman, 1968). As there is currently no literature on the integration or utility of GAS as an outcome measure for child life specialists, this paper will critically examine the available peer-reviewed literature to demonstrate how and why GAS is currently being implemented in pediatric rehabilitative settings by other health care practitioners. Results & Conclusion: Recommendations for the transferability of GAS in child life practice will subsequently be discussed to not only address this gap in knowledge, but to further emphasize the benefits of using an individual-ized outcome measure in clinical practice. Disclosure Statement: No potential conflict of interest was reported by the author(s). Funding Statement: No funding sources were provided by the author(s).
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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 | 0.066 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".