Barriers to Care Among High Emergency Department Users With Mental Disorders—A Mixed Methods Study
Bibliographic record
Abstract
The increasing number of high emergency department (ED) users is a growing concern worldwide. Patients with mental disorders (MD) are among the largest contributors to high ED use. As high ED use is often seen as an indicator of a healthcare system's shortcomings, high ED users with MD may perceive unique barriers to care. Analysing the associated patient characteristics and service use along with structural and motivational barriers to outpatient care use could help explain the high ED use among patients with MD, and help recommend more patient-centered interventions. Data were extracted from a 2021-2022 survey and medical records of 182 high ED users with MD in four large ED networks from Quebec (Canada), including open-ended questions administered to 20 of these patients. A mixed methods approach (multivariable regression, thematic analysis) identified variables associated with the number of unmet care needs, and structural and motivational barriers to care explaining high ED use. The study partially confirmed its first hypothesis that patients with more health issues who were dissatisfied with services would have more barriers to care. The study confirmed its second hypothesis: structural barriers were more prevalent than motivational ones in relation to high ED use, and patients with high unmet needs had more care barriers than those with no unmet needs. Findings suggest services could be greatly improved to reduce high ED use, and that unmet needs should be investigated further to better address the care barriers of 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 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".