“Hope is strong”: a qualitative inquiry into serious illness conversations for patients living with structural vulnerabilities and substance use disorders
Bibliographic record
Abstract
BACKGROUND: A serious illness conversation elicits a patient's wishes, goals and values in the setting of advancing illness. Patients living with structural vulnerabilities and substance use disorders encounter barriers to these conversations and therefore often experience less than ideal deaths. This study aims to understand the needs and preferences of these patients during serious illness conversations in the acute hospital setting and to inform best practice recommendations for serious illness conversations with this patient population. METHODS: We performed a qualitative research study using interpretive description methodology at a single tertiary care inner-city hospital in Vancouver, British Columbia, Canada. Data was collected from 16 hospitalized participants living with structural vulnerability, substance use disorder and chronic illness. Semi-structured interviews were recorded, transcribed and analyzed iteratively by our research team using thematic analysis. RESULTS: Participants had unmet basic needs and therefore unique priorities during serious illness conversations. Participants frequently had previous negative healthcare experiences which resulted in them guarding their feelings from healthcare providers. Hope was emphasized as an important component of serious illness conversations. Participants also outlined specific preferences and recommendations for healthcare providers engaging in these conversations. CONCLUSIONS: Our findings offer several important considerations for engaging in serious illness conversations with patients living with structural vulnerabilities and substance use disorders that, if implemented, should improve the quality of conversations and care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".