Bibliographic record
Abstract
The mental illness called bipolar disorder creates enormous social, economic and health implications for Canadian society. Bipolar disorder (BD) causes fluctuating mood states ranging from depression to mania. Over half a million Canadians live with BD (Schaffer, 2006), and approximately 400,000 are working age, 25-64 years (Wilkins, 2004). In Canada, reported prevalence of unemployment for all persons living with a severe mental illness, including BD, is as high as 90% (Kirby, 2006). The aim of this study was to explore the perspectives of people living with bipolar disorder regarding their employment accomplishments and obstacles, and to understand the adaptive strategies they used to manage both BD and employment. Using a qualitative research framework based primarily on the interpretive descriptive approach, considered especially suited to inform health care practice, purposive sampling identified 10 people living with the extreme mood fluctuations of BD type I. Their experiences were described during one in-depth interview, a follow-up telephone interview, and findings verified through written feedback. Three main themes were revealed, related to (1) hypomania and mania, (2) stigma and disclosure, and (3) employment factors that supported or created obstacles to successful employment. Findings suggest a broader perspective of employment issues should be considered by practitioners and employers to help reduce limitations encountered by this sample of persons with BD type I. Inclusion of social, psychoeducational and organizational issues in the repertoire of job accommodations may improve employment for this population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".