Bibliographic record
Abstract
Social service programs operate in an outcome-driven climate of program service delivery where external funding and regulating bodies demand outcome reporting. Administrative tools are needed to assist with decision-making that aims to improve program services and outcomes. In this article, we present Event History Analysis (EHA) as a statistical tool that can be used to investigate whether particular factors such as client or service characteristics lead to better outcomes in a family preservation program. The authors present a conceptual overview of EHA and discuss its utility as a diagnostic tool for program planning. Additionally, a demonstration of EHA is presented using Statistical Package for the Social Sciences (SPSS) software. The authors also illustrate how EHA can be used to explore program data beyond periodic counts of client outcome success and failure, and make meaningful time-sensitive changes to service delivery. Using three client characteristics that contribute to the risk of abuse: learning disability, family income, and family size, the article discusses the program and practice implications of these variables in working in a family preservation program. The article aims to provide a basic understanding of EHA for social service managers or administrators, since it is quickly becoming a common analytical method in social work research. Equipped with a conceptual understanding of EHA, administrators can direct their program evaluators to conduct EHA on program data.
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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 | 0.020 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".