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Psychosocial processes influencing weight management among persons newly prescribed atypical antipsychotic medications

2011· article· en· W2124370220 on OpenAlexaff
Sarah Xiao, Cynthia Baker, L. Kola Oyewumi

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychosocialAntipsychoticMedicinePsychiatryAtypical antipsychoticPsychologySchizophrenia (object-oriented programming)Clinical psychology

Abstract

fetched live from OpenAlex

Accessible summary The purpose of the study was to generate a theory related to the psychosocial processes of weight management among persons newly prescribed atypical antipsychotic medications, to develop better early intervention weight management programmes. Through 16 interviews with persons with first‐episode psychosis and schizophrenia, it was found that they faced a variety of barriers to weight management: inaccessibility of resources such as financial and geographical obstacles to healthier foods and exercise facilities, a lack of structure in their lifestyle, rapidity of weight gain following the initiation of the medication, insatiable hunger and a lack of supporting factors to increase their motivation. Many participants initially responded to the effects of weight gain by discontinuing their medications, choosing lower‐calorie foods, using walking in their daily activities as exercise, accepting their weight gain and trying to manage their weight but giving up. The consequences of these actions were that participants either contemplated but did not implement weight management, or did not attempt weight management at all. It is hypothesized that the theory developed through this study can assist with the understanding and management of weight gain among this population. Abstract The purpose was to generate a theory of the psychosocial processes influencing weight management among persons newly prescribed atypical antipsychotic medications. A grounded theory research design was used to guide the study. Semi‐structured interviews were the method of data collection, and analysis was performed using constant comparison. Using theoretical sampling, a sample of 11 participants with first‐episode psychosis prescribed atypical antipsychotics for at least 8 weeks, and five participants with a diagnosis of chronic schizophrenia prescribed atypical antipsychotic medication for at least 3 years were recruited from an outpatient psychiatric programme. Contextual factors influencing weight management were: accessibility to resources, unstructured lifestyle, and others' perception of weight. Conditions influencing weight management were: rapid weight gain, insatiable hunger and lack of motivation boosters. Participants' early responses to weight gain included discontinuing medications, choosing lower‐calorie foods, using walking in daily activities as exercise, accepting weight gain and trying to manage weight but giving up. The consequences revealed from data analysis were contemplating weight management and not trying, as the barriers to weight management exceeded the facilitators. The theoretical framework developed in this study can assist with the understanding and management of weight gain among this unique 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 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.336
Teacher spread0.313 · 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 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

Citations15
Published2011
Admission routes1
Has abstractyes

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