Experiences of housing insecurity among participants of an early childhood intervention programme
Bibliographic record
Abstract
OBJECTIVES: To understand the barriers to programme success among high-needs families in KidsFirst, an early childhood intervention programme. METHODS: Using a qualitative approach, a secondary analysis was performed using the qualitative data set (111 interviews and focus groups with 242 participants) from the KidsFirst programme evaluation. Data analysis was conducted to identify common experiences among high-needs families in the programme and barriers to programme success. RESULTS: Participants identified housing insecurity as a major factor impeding programme delivery, retention and successful outcomes. Housing insecurity was shown to create or exacerbate ongoing crises among high-needs families. Only after housing insecurity was addressed were families able to benefit from the KidsFirst programme. CONCLUSIONS: The findings of this research suggest that until baseline material security is established for high-needs families, early childhood development (ECD) interventions will be limited in meeting their objectives. In order to have the most effect for those living in poverty, helping families to achieve basic material security, including secure housing, should precede the targeted provision of psychosocial ECD supports. This finding has implications for how ECD intervention programmes could more effectively be designed and whom they should target.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".