Identifying factors which affect the diagnosis and treatment of youth with psychotic disorders in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Psychotic disorders are considered the most debilitating psychiatric conditions, and youth living with these disorders require significant supports to diagnose and treat their condition. Many youth rely on emergency services such as emergency departments (EDs) to manage their condition. This research sought to answer the question: What are identifiable factors affecting the diagnosis and treatment of psychotic disorders for youth in Newfoundland and Labrador (NL)? Three separate, though related studies were conducted. The first study examined variables affecting ED utilization for youth with a psychotic disorder in urban versus rural NL. The second examined the effect of COVID-19 on ED utilization for this population. Linear regression modelling was used in the analysis, with generalized estimating equations (GEE) required for study two. Both studies identified rural residence and sex as being associated with increased ED utilization for youth with a psychotic disorder in NL (p<0.05) in the multivariate analyses. ED utilization rose significantly (p=0.01) during the first year of COVID-19 then returned to levels similar to pre-pandemic utilization. Study three used qualitative methods to add to the quantitative findings. A grounded theory approach was used to collect and analyze data from interviews with clients of mental health services and family members, as well as focus groups with caseworkers. The study: 1) described patterns in clients’ developmental history, 2) identified challenges clients and family members experienced getting a diagnosis, managing medications, and receiving adequate support in the community, 3) identified gaps in services experienced by clients and family members and reported their recommendations. Caseworker participants recognized topics appearing in client and family member interviews as consistent with their experiences with this population in their practice. The results of this research identified existing gaps in services for this population using both quantitative and qualitative methods and reported the recommendations of family members and caseworkers as to how these gaps in service may be addressed. This research is intended to provide school staff, clinicians, caseworkers, and policymakers with the tools to strive to improve the services for one of the most vulnerable populations in our society.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".