Being a member of a novel transitional case management team for patients with unstable housing: an ethnographic study
Bibliographic record
Abstract
Abstract Background Homeless and unstably housed individuals face barriers in accessing healthcare despite experiencing greater health needs than the general population. Case management programs are effectively used to provide care for this population. However, little is known about the experiences of providers, their needs, and the ways they can be supported in their roles. Connect 2 Care (C2C) is a mobile outreach team that provides transitional case management for vulnerable individuals in a major Canadian city. Using an ethnographic approach, we aimed to describe the experiences of C2C team members and explore their perceptions and challenges. Methods We conducted participant observations and semi-structured interviews with C2C team members. Data analysis consisted of inductive thematic analysis to identify themes that were iteratively discussed. Results From 36 h of field observations with eight team members and 15 semi-structured interviews with 12 team members, we identified five overarching themes: 1) Hiring the right people & onboarding: becoming part of C2C; 2) Working as a team member: from experience to expertise; 3) Proud but unsupported: adding value but undervalued; 4) Team-initiated coping: satisfaction in the face of emotional strain, and; 5) Likes and dislikes: committed to challenges. Conclusions A cohesive team of providers with suitable personal and professional characteristics is essential to care for this complex population. Emotional support and inclusion of frontline workers in operational decisions are important considerations for optimal care and program sustainability.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".