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Record W2128263745 · doi:10.2147/ndt.s40777

Tackling nonadherence in psychiatric disorders: current opinion

2014· review· en· W2128263745 on OpenAlexaff
Saeed Farooq, Farooq Naeem

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2014
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePsychiatryExpert opinionCurrent (fluid)Family medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Nonadherence to treatment is a major challenge in all fields of medicine, and it has been claimed that increasing the effectiveness of adherence interventions may have far greater impact on the health of the population than any improvement in specific medical treatments. However, despite widespread use of terms such as adherence and compliance, there is little agreement on definitions or measurements. Nonadherence can be intermittent or continuous, voluntary or involuntary, and may be specific to single or multiple interventions, which makes reliable measurement problematic. Both direct and indirect methods of assessment have their limitations. The current literature focuses mainly on psychotic disorders. A large number of trials of various psychological, social, and pharmacologic interventions has been reported. The results are mixed, but interventions specifically designed to improve adherence with a more intensive and focused approach and interventions combining elements from different approaches such as cognitive-behavioral therapy, family-based, and community-based approaches have shown better outcomes. Pharmacologic interventions include careful drug selection, switching when a treatment is not working, dose adjustment, simplifying the treatment regimen, and the use of long-acting injections. The results for the most studied pharmacologic intervention, ie, long-acting injections, are far from clear, and there are discrepancies between randomized controlled trials, nationwide cohort studies, and mirror-image studies. Nonadherence with treatment is often paid far less attention in routine clinical practice and psychiatric training. Strategies to measure and improve adherence in clinical practice are based more on personal experience than on research evidence. This overview focuses on strategies used for improving treatment adherence in psychiatric disorders in the light of current evidence, with emphasis on public health aspects of treatment adherence and the management of nonadherence in routine clinical practice.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.003

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 head0.043
GPT teacher head0.374
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations77
Published2014
Admission routes1
Has abstractyes

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