Presenting the model of risk, disability and hard-to-reach families to inform early intervention services
Bibliographic record
Abstract
INTRODUCTION: Several concepts - risk, resilience, disability and hard-to-reach families in early intervention services - are talked and written about in many ways. Family Stress Theory can be usefully applied to explore these issues systematically. PROBLEM: The relationship between risk and disability is complex, and the role of resilience is not fully understood. The idea of "hard-to-reach families" is not well defined, thus presenting challenges to service providers and policy makers. Reflection: This paper presents the Model of Risk, Disability and Hard-to-Reach Families and uses the model to: (1) define the groups of high risk families and families of children with disabilities and explore the concept of resilience within these groups; (2) describe services offered to these groups; and (3) reflect on service use and so-called "hard-to-reach families". Each section includes suggested applications for service providers that may inform the work done with young children and their families who experience risk or disability. CONCLUSION: Service providers can apply the Model of Risk, Disability and Hard-to-Reach Families to consider each family's unique strengths and challenges, and use those individual elements to influence service recommendations and anticipate service use. Implications for rehabilitation The concepts of risk, resilience, and hard-to-reach families are poorly defined in the literatures, but have important implications with respect to early childhood intervention services. Family Stress Theory can help to identify high-risk families and account for family resilience It is important for clinicians, researchers and policy makers to consider the relationship between disability and risk with respect to services offered to families and the potential barriers to service use. Clinicians and policy makers have a role in promoting accessible early childhood services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".