Supporting clients on methadone maintenance treatment: a systematic review of nurse's role
Bibliographic record
Abstract
Accessible summary • Nurses, regardless of where they practice need to play a major role in providing holistic care for persons who participate in methadone maintenance treatment programmes. • Nursing tasks that were found to rank as the most important when caring for clients on methadone maintenance treatment were: to administer methadone, to observe the patient's general condition, to provide counselling, and offer ongoing support as needed. • Nurses should be prepared to assess for drug dependency, screen for opiate use when high index of suspicion is present and assess for psychosocial factors. Furthermore, nurses should be also knowledgeable about appropriate dosage of methadone and its efficacy, as well as support case management and outreach interventions. • Nurses need to know enough about addiction and methadone maintenance therapy to be able to gain an understanding of the client's attitude and perceptions regarding addiction and the treatment process. This paper discusses findings from a systematic review of literature pertaining to methadone maintenance in relation to the role of the nurse working in a general practice setting. Five electronic databases were searched for period of 2000–2008: Medline, CINAHL, Embase, PsycINFO and Cochrane Database of Systematic Reviews. Included studies were peer-reviewed literature articles that: (1) were dated no earlier than the year 2000; (2) represented a primary study (qualitative and quantitative), systematic review or meta-analysis; (3) focused on methadone maintenance for treatment of opiate dependence, not for pain; and (4) were published in English. In this review, three broad themes emerged as being significant to the role of the nurse in relation to methadone maintenance treatment (MMT) programming. These themes included: (1) identifying the client's personal characteristics; (2) having knowledge about methadone; and (3) supporting clients in MMT programmes. In conclusion, it is important to address the needs of health professionals working with clients on MMT, in order for them to provide the necessary care for this patient population. In particular, nurses need to gain knowledge about MMT to assist them in carrying out their nursing role competently when caring for this vulnerable population.
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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".