Approaches to evaluation of homelessness interventions
Bibliographic record
Abstract
Purpose – The purpose of this paper is to provide rationale, methodological guidance and clarity in the use of case study designs and theory driven approaches to evaluation of interventions to end homelessness. Design/methodology/approach – Using an evaluation of a transitional shelter program aiming to support permanent exits from homelessness as an example, the authors show how case study designs and theory driven evaluation is well suited to the study of the effectiveness of homelessness interventions within the broader socio-political and economic context in which they are being implemented. Findings – Taking account of the context as part of program evaluation and research on homelessness interventions moves away from blaming programs and individuals for systemic failures to better understanding of how the context influences successes and failures. Case study designs are particularly useful for studying implementation and the context which influences program outcomes. Theory driven evaluations and the use of realist evaluation as an approach can provide a broader understanding of how homelessness interventions work particularly for whom and under what conditions. These methodological and theoretical approaches provide a consistent strategy for evaluating programs aimed at ending homelessness. Originality/value – There is a need for greater capacity in the homelessness sector to apply approaches to evaluation that take into account the broader socio-political and economic context in which programs are being implemented. Through the use of a case example, the authors provide guidance for application of case study design and theory driven approaches as a strategy for approaches programs aimed at ending homelessness.
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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 | 0.196 | 0.207 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".