Linking user and staff perspectives in the evaluation of innovative transition projects for youth with disabilities
Bibliographic record
Abstract
A key challenge in formative evaluation is to gather appropriate evidence to inform the continuous improvement of initiatives. In the absence of outcome data, the programme evaluator often must rely on the perceptions of beneficiaries and staff in generating insight into what is making a difference. The article describes the approach adopted in an evaluation of 15 innovative projects supporting school-leavers with disabilities in making the transition to education, work and life in community settings. Two complementary processes provided an insight into what project staff and leadership viewed as the key project activities and features that facilitated successful transition as well as the areas of quality of life (QOL) that participants perceived as having been impacted positively by the projects. A comparison was made between participants' perceptions of QOL impact with the views of participants in services normally offered by the wider system. This revealed that project participants were significantly more positive in their views than participants in traditional services. In addition, the processes and activities of the more highly rated projects were benchmarked against less highly rated projects and also with usually available services. Even in the context of a range of intervening variables such as level and complexity of participant needs and variations in the stage of development of individual projects, the benchmarking process indicated a number of project characteristics that were highly valued by participants.
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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 | 0.249 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".