Mothering through a child's addiction journey: Linking lived experience to the lenses that shape intervention
Bibliographic record
Abstract
Summary Someone dies from an opioid overdose every two hours in North America. These statistics became personal when my eldest son died from a drug overdose—he had been in recovery for a year, excited about the prospect of welcoming his unborn daughter into the world, and desperately committed to staying drug-free. He had been struggling with substance abuse for over twelve years. As a mother and Social Worker, I spent those years feeling helpless and deficient as things spiraled further and further out of control, committed to supporting my beloved son in a system that was not working. Findings This article is about this journey. My goal is to critically examine how our treatment lenses for understanding addiction create unacknowledged ethical issues and tensions that stigmatize not only those with substance use issues, but their family as well. Application The purpose is two-fold: to examine how the experience of mothering a child with addictions who dies is constructed as a problematic, and to invite Social Workers to think critically about their practices and the lenses they are drawing on when working with this population.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".