Supporting Youth Towards Positive Mental Health
Bibliographic record
Abstract
Loneliness is a recognized problem amongst the elderly, but it is fast becoming a serious concern \namongst the younger generation. Despite social media and friends on face book being an integral \npart of their existence, chronic loneliness has been described as the silent plague that is hurting \nyoung people more than any other age group (Gil, 2014). In the light of the recent acknowledgement \nthat Canada’s mental health service is already failing to care for large numbers of young people, \nmany mental health organizations have turned to digital communication systems to reach vulnerable \nyouth. Although this generation is more digitally connected than any previous cohort, using a digital \ncommunication channel to treat loneliness and depression seems counterintuitive. Taking a systems \nand design thinking approach, this study takes a critical look at the potential causes and complex \nissues surrounding chronic loneliness, and the link to depression and mental illness for young \npeople, to inform potential preventative interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".