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Record W3047715408 · doi:10.2147/ndt.s248027

<p>Interpersonal Psychotherapy for Late-life Depression and its Potential Application in China</p>

2020· review· en· W3047715408 on OpenAlexafffund
Hua Xu, Diana Koszycki

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2020
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsInstitut du Savoir MontfortMontfort HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchScience and Technology Commission of Shanghai Municipality
KeywordsMedicineDepression (economics)ChinaInterpersonal psychotherapyInterpersonal communicationPsychotherapistPsychiatryInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

Interpersonal psychotherapy (IPT) is a time-limited, structured, interpersonally oriented psychotherapy, with demonstrated efficacy for the treatment of major depression across the lifespan. IPT uses a medical model of illness and links depressed mood to four research-informed interpersonal problem areas: complicated grief, role transitions, role disputes, and interpersonal deficits/sensitivity. The IPT model of vulnerability to depression nicely dovetails with interpersonal issues that are faced by older adults, and this article focuses on the application of IPT for late-life depression in China. The group format of IPT may be a practical and efficient method of improving access to an established depression-focused treatment for China's rapidly aging population and has the advantage of providing important social support for patients who feel lonely, isolated, and stigmatized. Short-term interventions like IPT are more cost-effective from a public health perspective and can easily be delivered in primary care facilities, where many elderly patients receive care. IPT is effective in different cultures, and possible cultural adaptations of IPT for older adults in China are discussed herein.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.301
Teacher spread0.285 · 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 teacher head, not a consensus.

Study designOther design
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

Citations18
Published2020
Admission routes2
Has abstractyes

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