Exploring the impact of social support and chronic distress on drug addiction severity
Bibliographic record
Abstract
The present study investigated whether the relationship between social support and drug \nuse severity is mediated by one’s level of psychological distress. Scales measuring \nchronic distress, social support, and drug abuse were administered to a sample of drug \nusers attending an outreach service in St. John’s, NL (n = 50). In conjunction, a subset of \nmatched participants was extracted from the Canadian Community Health Survey \n(CCHS) database (n = 25); these participants were also self-reported drug users and \nanswered questions pertaining to the study variables. Initial bivariate analyses determined \nthat further tests of mediation were not warranted due to a lack of significant correlation \namong the study variables. However, follow-up comparisons indicated that drug users \nwithin St. John’s, NL were significantly more distressed, had lower social support, and \ngreater severity of drug use compared to the overall CCHS population. Extreme severity \nof distress and drug abuse were consistent across the sample, therefore, a lack of \nvariability among these factors might explain the lack of significant results. Since the \nmental health status of this sample was so poor, it is recommended that they be treated as \na unique study group, or receive treatment prior to future research on this topic.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".