The Protective Association between Pet Ownership and Depression among Street-involved Youth: A Cross-sectional Study
Bibliographic record
Abstract
Street-involved youth represent a particularly vulnerable subsection of the homeless population and are at increased risk of health problems, substance abuse, and depression. Qualitative research has demon- strated that animal companions help homeless youth cope with loneliness, are motivators for positive change, such as decreasing drug or alcohol use, provide unconditional love without judgement, and improve youths’ sense of health. To quantitatively investigate the association between depression and pet ownership among street-involved youth, a cross-sectional study was per- formed with a convenience sample of 189 street-involved youths who were surveyed in four cities in Ontario, Canada, 89 of whom were pet owners and 100 of whom were not. Logistic regression modelling found pet ownership to be negatively associated with depression in the study population (controlling for gender, regular use of drugs, and time since youth left home), with the odds of being depressed three times greater for youths who did not own pets. While pet ownership among street-involved youth has many liabilities, includ- ing impairing youths’ ability to access shelter, services, and housing and employment opportunities, companion animals may offer both physical and psychosocial benefits that youth have difficult attaining. This finding highlights the importance of increased awareness among youth service providers of the potential impacts of pet ownership for street-involved youth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".