Examining the impact and implementation of the ENabling VISions And Growing Expectations (ENVISAGE) program in Croatia: a discourse analysis pilot study
Bibliographic record
Abstract
Purpose To explore the impact of the online ENVISAGE program for parents of children with neurodevelopmental disabilities (NDD) on parents’ perception of themself, their child with a disability, and their family, as well as to explore experiences of participating in the program in Croatia.Methods In this before-after discourse analysis study, participants took part in the five-week ENVISAGE program. There were two semi-structured interviews for each participant: within one month before and after participating in the program. The proportions of positive, neutral, and negative sentences about themself, their child, and their family from two interviews were compared on an individual and group level. The perceived changes and experiences with the program were also analyzed qualitatively.Results Data from thirteen participants were included. From the three pre-determined discourse categories (self, child, and family), most changes were observed in parents’ perception of self (average increase in positive views of 8.8% and decrease in negative of 5.3%). Qualitative results showed multiple positive self-perceived impacts on parents’ lives. Participants’ experiences with ENVISAGE were consistently positive; all believed they benefited from the program.Conclusions The results support our assumption that participation in ENVISAGE positively affects multiple areas of life, particularly parents’ views of themself.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".