Learning from parents' stories about what works in early intervention
Bibliographic record
Abstract
Using a multiple case study approach, this ethnography examined the experiences of parents of children deemed at risk for developmental delays or disabilities who had received early intervention (EI) services (birth to age 3 years) in a large urban location in Western Canada. Participants (11 adult parents and 7 children) were drawn from six families. Methods of data collection included focus groups (FG), face-to-face interviews and file reviews. Member check and expert reviews were conducted throughout data collection and data analyses as part of the validation process in this ethnography. Qualitative content analyses followed by thematic analyses highlighted the implementation of family-centred practices (FCP) as a main theme. Parents identified how EI professionals using FCP embraced collaborative practices. FCP resulted in parents leading the EI process for their children. More specifically, EI professionals shared strategies and information to support parents in gaining a deeper understanding of their children's individual developmental characteristics. Parents expressed how empowering this level of understanding was for them as they learned to articulate what were their children's needs for developmental, health and educational services. Recommendations for future research include inquiring about parents' experiences for families of diverse constellations and/or residing in smaller urban or rural communities.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".