Career Decision-Making Processes of Individuals with First-Episode Psychosis
Bibliographic record
Abstract
Background: Psychosis is a disabling condition which results in an individual losing contact with reality. The first episode of psychosis (FEP) often emerges during adolescence or early adulthood, when individuals are pursuing important educational and career milestones, which can become derailed due to the development of major impairments. Knowledge of the career decision-making processes of individuals with FEP is limited. This information would enable vocational rehabilitation services to improve the social and career participation of individuals with FEP. Thus, this study advanced a model of the career decision-making processes of individuals with FEP and the influences that impacted these processes. Method: Strauss and Corbin’s Grounded Theory method was used to guide the study design, sampling and analysis. Three early intervention programs in Ontario, Canada were included in this study. Participants included 36 individuals who were either treated for FEP by an early intervention team, program staff who had direct client contact, or family members of individuals with FEP. Data collection included sociodemographic questionnaires, one-on-one interviews, program documents, case-based memos, and a reflexive journal. Analysis began with open coding, proceeded to axial coding, and terminated with selective coding, which resulted in an explanatory model. Results: The career decision-making processes of individuals with FEP is a multi-staged, iterative process of making a career choice based on exploration, evaluation of options, and taking actions that moves them towards their career goals. These processes unfolded over five phases of illness and recovery and are impacted by internal and environmental influences. During the onset of symptoms, participants disengage from career decision-making but reengage during recovery. For individuals with substance abuse issues or co-existing conditions active engagement in career decision-making stalled until they quit using substances or co-existing conditions are managed. Implications: This study’s findings suggest that vocational outcomes of individuals with FEP could be improved, if initiatives consider an individual’s phase of illness and stage of career decision-making. Future studies should assess the comprehensiveness of this model in other jurisdictions, and explore the impact of gender, psychosis onset, and vocational interventions on career decision-making.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".