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Self-management techniques for bipolar disorder in a sample of New Zealand Chinese

2009· article· en· W2083417086 on OpenAlexaff
Grace Wang, Samson Tse, Erin E. Michalak

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarmony (color)PsychosocialPsychologyTaoismMulticulturalismCoping (psychology)Psychological interventionInterpersonal communicationBipolar disorderSocial psychologyClinical psychologyPsychotherapistPsychiatryBuddhismMood

Abstract

fetched live from OpenAlex

Aims The aim of the study presented in this article was to consider how New Zealand Chinese with bipolar disorder manage their condition, regain and maintain wellness through the use of self-management techniques. Methods Nine New Zealand Chinese with bipolar disorder type I or II who had reasonable performance in role functioning were interviewed. Data analysis was guided by the inductive approach. Findings In contrast to Western psychosocial interventions, which emphasize the individual's independence, self-advocacy and self-identity, New Zealand Chinese are more likely to value themselves through relationships with others. Most participants emphasized the importance of harmony with self and others, and adopted passive and nature-oriented attitudes encouraged by Taoism to deal with life stress. Strategies of ‘taking it easy’ and ‘looking at problems in others’ shoes' were frequently used when dealing with interpersonal conflicts. Conclusions The concepts of health and life as part of traditional Chinese culture were found to be the fundamental elements influencing the participants' coping patterns. There is a strong need for facilitating the connection between health professionals and clients. This study indicates that to do this, health professionals must be aware of the importance of cultural sensitivity when delivering health care in a multicultural environment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.007
GPT teacher head0.306
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2009
Admission routes1
Has abstractyes

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