Factors Affecting the Psychiatric Patients’ Wait Time at Erie Shores Health Care: A Qualitative Enquiry
Bibliographic record
Abstract
Background: Erie Shores HealthCare (ESHC) is a rural, resource-poor, publicly funded, 72-bed hospital serving South-West Ontario region.Psychiatric patients' timely access to care is an important indicator for the quality of care at the ESHC.This qualitative inquiry focused on the wait time of the psychiatric patients attending the ESHC emergency department.The primary objective was to interpret healthcare providers' (informants) perspectives on possible factors affecting the wait time of the psychiatric patients, awaiting transfer or clinical care, at the hospital.Methods: A series of in-depth interviews were conducted with the informants following Thorne's Interpretive Description approach.Thematic analysis was conducted to analyze and to interpret interview transcripts.Results: Emerged cross-cutting themes were: 1) admission process, 2) transfer process, 3) patient factors, 4) staff factors, and 5) available resources.Informants also recognized a knowledge gap in frontline workers on currently available mental health resources in the area.Conclusion: This qualitative inquiry has allowed Erie Shores HealthCare develop a one pager practical for frontline health workers in a rural setting to navigate the available community resources.Further research can validate our findings and may allow other rural hospitals to seek solutions to similar issues.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".