Using financial incentives to support service engagement of adults experiencing homelessness and mental illness: A qualitative analysis of key stakeholder perspectives
Bibliographic record
Abstract
INTRODUCTION: Homelessness and mental illness are associated with poor service engagement, health and health service use outcomes. Existing literature suggests that financial incentives may effectively support service engagement of this population, but studies investigating key stakeholder perspectives are lacking. This study aimed to elicit, using qualitative methods, nuanced service user and provider experiences by using financial incentives to support service engagement among adults experiencing homelessness and mental illness. METHODS: This qualitative study is part of a larger mixed-methods pragmatic trial of financial incentives (Coordinated Access to Care for the Homeless-Financial Incentives [CATCH-FI]) within a community-based brief case management programme (CATCH) in Toronto, Ontario. Twenty-two CATCH-FI participants were purposefully recruited to participate in in-depth, semi-structured interviews; five CATCH service providers participated in a focus group and seven key informants in individual interviews. Data collection occurred between April 2019 and December 2020. All interviews and the focus group were audio-recorded and transcribed. Topic guides prompted participant perspectives on and experiences of using financial incentives to support engagement, health and well-being. Grounded theory and inductive thematic analysis guided coding and interpretation of transcripts. Triangulation and member-checking enhanced the analytical rigour and validity of findings. RESULTS: CATCH service providers, key informants and subgroup of CATCH-FI participants perceived financial incentives to directly facilitate service engagement. The majority of CATCH-FI participants however highlighted that intrinsic motivation and service quality may be relatively more important facilitators of engagement. Most study participants across stakeholder groups perceived that financial incentives have direct positive influences on health and well-being in enabling access to basic needs and simple pleasures. CONCLUSIONS: Our data suggest that for some adults experiencing homelessness and mental illness, financial incentives can directly support service engagement. In addition, financial incentives may positively impact health and well-being by easing financial stress and enabling deeper attention to individual health needs. Further research on the effectiveness and acceptability of financial incentives is needed to improve understanding and uptake of a promising intervention to support health and health service use outcomes in an underserved population. PATIENT OR PUBLIC CONTRIBUTION: Study participants provided input into the study research questions, study design, interview guides and interpretation of findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".