Relationship between depression and social ties / by Ashley Percival.
Bibliographic record
Abstract
Imagine having an illness that stripped away your identity, had the ability to impair or \ndestroy valuable relationships, and left you crippled with sadness and anxiety (Canadian Mental \nHealth Association [CMHA], 2010b; Public Health Agency of Canada [PHAC], 2010b). It \nwould be overwhelming, to say the least. Added to that is the fact that the rates of relapse are \nhigh for this illness, and for some sufferers death is inevitable (Beattie, Pachana & Franklin, \n2010; CMHA, 2010b; Jhingan as cited by Rajkumar, Thangadurai, Senthilkumar, Gayathri, \nPrince & Jacob, 2009). Tragically, those who cannot cope with this illness may commit suicide, \nif they do not die from related physical causes (Alexopoulos, 2005; Beattie et al., 2010; Bephage, \n2005; Chew-Graham, 2010; CMHA, 2010b; Gilmour, 2010; Golden, Conroy, Bruce, Denihan, \nGreene, Kirby, et al., 2009). The name of this illness? Depression. \nDepression is an elusive mental illness. Three million Canadians will experience \ndepression in their lifetime from various causes (CMHA, 2010b). But, there is no single cause \nfor this condition. Researchers indicate that chemical imbalances in the brain, medications, \nphysical conditions, psychosocial and socio-economical factors may be among the potential \ncauses of depression (Alexopoulos, 2005; Beattie et al., 2010; Butcher & McGonigal-Keimey as \ncited by Costa, 2006; Chew-Graham, 2010; Cicirelli, 2009; Cyr, 2007; Grundy, 2006; PHAC, \n2010c; Yohannes & Baldwin, 2008).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".