Outcome evaluation of an early psychosis program using pre-post comparison and propensity matching
Bibliographic record
Abstract
Early Psychosis Programs have been developed as a solution to reduce delays and improve outcome for first episode psychotic patients. Evaluations of the programs worldwide have found that the programs help reduce symptoms and hospitalizations and improve quality of life. The purpose of this thesis is to evaluate the overall impact of the Newfoundland and Labrador Early Psychosis Program (NL Program) for its patients. Traditionally, programs are evaluated by pre-post methodology. However, this method may have limitations since it does not use standard control groups and any changes seen in the patients from entry to completion of the program may be due to natural events, such as maturation and changes in hormones, since the patients tend to be fairly young. Therefore, this study will test a novel methodology, propensity matching, as an alternative method to evaluate the NL Program. The patients are matched to a national population from the Canadian Community Health Survey (CCHS) on several clinical and social outcomes to determine how the patients differ from the general population at entry into the NL Program and then after two years, to see if they converge back to the population after completing the NL Program. Propensity matching results are then compared to the pre-post results. Analysis of the data found the propensity matching methodology did produce similar results as the pre-post evaluation approach on social and clinical outcomes such as reducing substance use, depression, hospitalizations and suicide, and improving quality of life and vocational functioning. In conclusion, this study found the NL Program is having a major treatment effect for its patients, and propensity matching may serve as a model in future evaluations in mental health research.
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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 | 0.034 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".