Bibliographic record
Abstract
Background: Mental health illnesses, such as depression, are responsible for a growing disease burden worldwide. Unfortunately, effective treatment is often impeded by stigmatizing attitudes of other individuals, which have been found to lead to a number of negative consequences including reduced help-seeking behavior and increased social distance. Despite the high prevalence of depression in Canada, little research has been conducted to examine stigma against depression in the Canadian general population. Such information is crucial to understanding the current state of stigmatizing attitudes in the Canadian communities, and framing future stigma reduction initiatives. The objectives of this study were to estimate the percentages of various stigmatizing attitudes toward depression in a general population sample and to compare the percentages by demographics and socioeconomic characteristics. Methods: We conducted a cross-sectional telephone survey in Alberta, Canada, between February and June 2006. Random digit dialing was used to recruit participants who were aged 18-74 years old (n = 3047). Participants were presented a case vignette describing a depressed individual, and responded to a 9-item Personal Stigma questionnaire. The percentages of stigmatizing attitudes were estimated and compared by demographic and socioeconomic variables. Results: Among the participants, 45.9 % endorsed that depressed individuals were unpredictable and 21.9 % held the
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.393 | 0.209 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".