Addiction and Substance Abuse: Working with Patients and Their Families
Bibliographic record
Abstract
Addiction is a chronic, relapsing disease, which is most likely to manifest when a person with a genetic predisposition is raised in an environment that fails to meet his or her need for connection and safety.1 It often co-arises with major mental illness and should be a red flag alerting the physician to a history of trauma and/or adverse childhood experiences. Children who grow up in families plagued by addiction often experience difficulties with self-regulation and in their relationships with others. They tend to form traumatic attachments, which perpetuate the cycle from one generation to the next. Effective treatment must therefore include the family, not just the affected individual. Unfortunately, many people with addictions report negative experiences in health care settings, including in primary care. These negative experiences can lead them to stop using health care services. On the other hand, patients state that the most important determinant of a successful treatment outcome is a strong and supportive relationship with their primary caregiver, who can advocate for and assist them as they navigate the health care system.2 Family physicians (FPs) are in an ideal position to provide integrated care for patients with mental health and addiction problems because they are accessible, they are experts at managing chronic disease, they have longitudinal relationships with patients, and they look after families.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".