Profiles of quality of outpatient care among individuals with mental disorders based on survey and administrative data
Bibliographic record
Abstract
RATIONALE: Though it is crucial to contribute to patient recovery through access, diversity, continuity and regularity of outpatient care, still today most of these are deemed nonoptimal. Identifying patient profiles based on outpatient service use and quality of care indicators might help formulate more personalized interventions and reduce adverse outcomes. AIMS AND OBJECTIVES: This study aimed to identify profiles of individuals with mental disorders (MDs) patterned after their outpatient care use and quality of care received, and to link those profiles to individual characteristics and subsequent outcomes. METHODS: A cohort of 5669 individuals with MDs was considered based on data from the 2013-2014 and 2015-2016 Canadian Community Health Survey, which were linked to administrative data from the Quebec health insurance registry. Latent class analysis generated profiles based on service use over the 12 months preceding each respondent's interview, and comparative analyses were used to associate profiles with sociodemographic and clinical characteristics, and health outcomes over the three following months. RESULTS: Four profiles were identified. Profile 1 (P-1) was labelled 'Low service use'; P-2 'Moderate general practitioner (GP) care and continuity and regularity of care'; P-3 'High GP care, continuity and regularity of care, and low psychiatrist care'; and P-4 'High psychiatrist care and regularity of care, and low GP care'. Profiles 3 and 4 (~50% of the cohort) were provided with better care, but showed worse outcomes, mainly acute care use due to more complex conditions and unmet needs. Profiles 1 and 2 had better outcomes as they showed fewer risk factors such as being younger and having better social conditions. CONCLUSION: Intensity, diversity and regularity of care were higher in profiles with more complex MDs, chronic physical illnesses, and worse perceived health conditions. Adapting specific interventions for each profile, such as assertive community treatment or intensive case management for Profile 4, is recommended.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".