Abandonment
Bibliographic record
Abstract
Purpose: Emergency departments (ED’s) often serve as the access point to health services for individuals living with mental health challenges, with mental health crisis (MHC) accounting for 15% of all presentations to ED’s in Canada. Consumers’ experiences of emergency mental health services have widely been reported as negative. This research aims to explore the experiences of individuals accessing the ED for MHC.
 Method: A supra-analysis was conducted using data from four semi-structured interviews collected from a larger study exploring stigma, discrimination and resilience in people experiencing mental health challenges. Supra-analysis aims to explore an aspect of the data from a different theoretical perspective. Transcripts were selected based on a participant history of voluntarily accessing emergency services for MHC. Data analysis was completed using the process of thematic analysis which involved immersion in the data, the development and refinement of codes leading to themes.
 Findings: A major theme of abandonment was identified in participant interviews with subthemes of; geographic, socioemotional and therapeutic abandonment. Participants reported that the locations of care, lack of social/emotional engagement and lack of health care providers’ (HCP) knowledge led to negative experiences attending ED’s. Participants also reported a lack of desire to access emergency services in the future.
 Conclusion: Future research is vital to enhance the delivery of emergency services, to reduce the feelings of abandonment experienced by individuals accessing the ED for MHC. Training and education must be provided to HCP’s staffing ED’s that focuses on providing high quality, appropriate emergency services to this vulnerable population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".