Are Methadone Counselors Properly Equipped to Meet the Palliative Care Needs of Older Adults in Methadone Maintenance Treatment? Implications for Training
Bibliographic record
Abstract
Today's methadone patients differ greatly from those of the past. Because of the rise of polydrug use and the HIV and hepatitis epidemics, treatment has become much more complex, which multiply the concerns and complexities of treatment. Patients entering methadone programs are also more commonly presenting at ages well into their 50s, 60s, and 70s; and this phenomenon of high rates continues to grow. The majority of these individuals in treatment have presented with a number of significant comorbid medical conditions that will progress and eventually lead to death. This aging cohort must be approached with a modified treatment plan that focuses on management and promoting healthy aging, while attending to their maximum delay of illness, disease, and disability. This article argues that it is necessary for counselors working with this group to adopt a palliative care philosophy. This article also makes recommendations in areas that counselors need to be knowledgeable and skilled in to provide appropriate palliative services specific to this aging population with multiple needs as they near end of life.
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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".