Perceived Case Management Needs and Service Preferences of Frequent Emergency Department Users: Lessons Learned in a Large Urban Centre
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explore the service needs and preferences of frequent emergency department users with mental health and addictions concerns who participated in a brief intensive case management intervention. METHODS: We conducted semi-structured individual interviews with 20 frequent emergency department users with mental health and addictions challenges, 13 service providers involved in the delivery of a brief case management intervention, and a focus group with intervention case managers. Thematic analysis was used to explore perceived service user profiles, service needs and preferences of care. RESULTS: Service users experienced complex health and social needs and social isolation, while exhibiting resilience and the desire to contribute. They described multiple instances of stigmatization in interactions with healthcare professionals. Components of the brief intensive case management intervention perceived to be helpful included system navigation, advocacy, intermediation, and practical needs assistance. Frequent service users valued relational responsiveness, a non-judgmental stance, and a recovery orientation in case managers. CONCLUSION: Interventions for frequent service users in mental health may be enhanced by focusing on the engagement of formal and informal social supports, practical needs assistance, system navigation, advocacy and intermediation, and attention to the recovery goals of service users.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".