Promoting Family Resilience through ASD Diagnostic Assessment: An Enhanced Critical Incident Technique Study
Bibliographic record
Abstract
This study will attempt to bridge the gap between the theoretical knowledge of family resilience and the practical implementation of this knowledge. The primary objective, in conducting this study, is to better understand how families with children with ASD can capitalize on their strengths by promoting family resilience. Interviews were conducted with 12 caregivers of children recently diagnosed with ASD in Alberta, Canada. Enhanced Critical Incident Technique (ECIT) was used to analyze the interview data. Data analysis resulted in 18 helping categories, 13 hinder categories, and 10 wish list categories that encapsulated the 274 incidents/wish items identified by the participants. These categories were then further divided by relevance for professionals (Part I) and for parents/families (Part II). The categories in Part I were synthesized with existing literature to conclude, (a) practical and resource-based recommendations, (b) emotional support recommendations, and (c) systemic recommendations for professionals. The categories in Part II were also presented within the context of the current literature and external resilience-enhancing and resilience-challenging influences are presented, as well as internal resilience-enhancing and resilience-challenging influence are discussed.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".