Adverse childhood experiences in a pathway to single adult homelessness in Hamilton, New Zealand
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) can contribute to housing instability and risk of homelessness, featuring disproportionately in the life histories of many people experiencing homelessness. However, little is known about the prevalence and lifelong impact of ACEs among people experiencing homelessness in New Zealand, a country experiencing increasing homelessness levels amid a housing affordability crisis. Drawing on data from 100 questionnaire surveys and 11 interviews with participants registered with The People’s Project, a Housing First homeless service in Hamilton, we explore the prevalence and role of ACEs in homeless journeys, identifying a common pathway to homelessness among participants. Some varying factors contributing additionally for Māori (indigenous people) were identified. Our findings showed ACEs were commonly reported by participants, often preceding a series of disruptive events across participants’ lives. Accumulations of adverse events, coupled with structural and other constraints, contributed to housing insecurity across lifespans. Results highlight the importance of trauma-informed homelessness initiatives, such as Housing First, as well as measures aimed at reducing upstream drivers of homelessness such as poverty, structural racism, and the ongoing impacts of colonisation in New Zealand.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".