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Using Museum Objects to Improve Wellbeing in Mental Health Service Users and Neurological Rehabilitation Clients

2013· article· en· W1994642578 on OpenAlexaff
Erica Ander, Linda Thomson, Kathryn Blair, Guy Noble, Usha Menon, Anne Lanceley, Helen J. Chatterjee

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsWomen's Health Research Institute
FundersArts and Humanities Research CouncilUniversity of Oxford
KeywordsOccupational therapyRehabilitationPsychologyMental healthDistractionFacilitationIdentity (music)Art therapyQualitative researchNursingPsychotherapistApplied psychologyMedicinePsychiatrySociologyAesthetics

Abstract

fetched live from OpenAlex

Introduction: The study investigated the impact of museum object handling sessions on hospital clients receiving occupational therapy in neurological rehabilitation and in an older adult acute inpatient mental health service. Methods: The research used a qualitative approach based on objectivist and constructionist methods, from which themes typical of the object handling sessions were derived. Results: Themes emerging from detailed analysis of discourse involving clients (n = 82) and healthcare staff (n = 8) comprised: distraction and decreasing negative emotion; increasing vitality and participation; tactile stimulation; conversational and social skills; increasing a sense of identity; novel perspectives and thoughts; learning new things; enjoyment and positive emotion. Critical success factors included good session facilitation for mitigating insecurity, ward staff support and the use of authentic heritage objects. Conclusion: Museums and their collections can be a valuable addition to cultural and arts occupations, in particular for long-stay hospital clients.

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 score0.796
Threshold uncertainty score0.380

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 communication0.0000.001
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 head0.072
GPT teacher head0.336
Teacher spread0.265 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

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