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Record W4415028756 · doi:10.11606/003250168

Pharmacists' clinical activities and impact on the care of incarcerated people at correctional settings : a scoping review protocol

2024· dissertation· en· W4415028756 on OpenAlexaboutno aff
Christian Eduardo Castro Silva

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Vulnerability (computing)Grey literatureProtocol (science)Mental healthHealth care

Abstract

fetched live from OpenAlex

INTRODUCTION: There are around 11 million people incarcerated worldwide. Due to social vulnerability and the issues surrounding the correctional system, this population is more susceptible to infectious diseases (such as HIV/AIDS, HCV, and tuberculosis) and the development of noncommunicable chronic conditions (including diabetes mellitus, hypertension, and mental disorders). As part of the healthcare team, pharmacists can provide clinical services to help inmate-patients achieve optimal health outcomes while minimizing the risk of harm. OBJECTIVE: To map and synthesize the evidence regarding the clinical activities provided by pharmacists and their impact on outcomes of care for incarcerated individuals in correctional settings. MATERIALS E METHODS: This scoping review was conducted following the recommendations of the PRISMA-ScR. The search was conducted in PubMed, Scopus, and LILACS databases up to July 30, 2024. Gray literature was explored (Google Scholar), and references of included articles were also reviewed. Articles focusing on pharmacists' clinical activities in correctional facilities were selected, excluding survey studies, book chapters, dissertations and thesis, editorials, literature reviews, guidelines, papers published before 2000, and 1f written in languages other than English or Portuguese. Using the Rayyan QCRI software, duplicates were removed, and a first screening was conducted by two assessors independently reviewing titles and abstracts, followed by a second screening involving full-text reviews, with all disagreements resolved by the advisor. For each included article, data such as author, year, clinical activities, and the pharmacists' impact were extracted. RESULTS: The literature search identified 894 studies, leading to the inclusion of 20 articles published from 2010 to 2023 that met the criteria for||this review. All the articles included originated from high-income countries, with most articles were from the USA (65%), and some from Canada, France, and Ireland. The studies primarily involved correctional settings, at any level of structure or territorial and administrative organization, with varying population profiles, primarily adult males. Health issues focused on infectious diseases like HIV/AIDS and HCV, as well as chronic conditions such as diabetes, and mental disorders. Pharmacists conducted various clinical activities, notably medication management and patient education. Fourteen studies reported positive outcomes related to pharmacists' roles in identifying drug interactions and advising on treatments. The impact of pharmacists on care outcomes was significant, demonstrating improvements in patient health and treatment effectiveness. CONCLUSION: This scoping review highlights the role of pharmacist on the care of the incarcerated population, focusing on medication management, patient education, and collaborative practice. Pharmacist-led interventions improved health outcomes for incarcerated individuals, particularly for conditions like diabetes and HIV/AIDS, similar to those in non-incarcerated populations. It emphasizes the need for more research in low- and middle-income countries and on women's health issues and other prevalent conditions in prisons

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 imitation

Not 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.

metaresearch head score (Codex)0.077
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.077
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.058
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0120.013
Bibliometrics0.0250.017
Science and technology studies0.0040.004
Scholarly communication0.0080.007
Open science0.0060.007
Research integrity0.0080.004
Insufficient payload (model declined to judge)0.0430.007

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.

Opus teacher head0.121
GPT teacher head0.548
Teacher spread0.427 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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