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Record W4320076675 · doi:10.53841/bpsneur.2022.1.13.22

Staff perceptions of patient peer relationships on a male neuropsychiatric rehabilitation unit

2022· article· en· W4320076675 on OpenAlexaff
Eleanor Green, Lorraine Bobbie Turnbull

Bibliographic record

VenueThe Neuropsychologist · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsSuperordinate goalsThematic analysisPsychologyEmpathyPerceptionRehabilitationPeer supportSocial psychologyQualitative researchPsychiatry

Abstract

fetched live from OpenAlex

This research investigated peer relationships within an inpatient neuropsychiatric rehabilitation service. Seven support workers were recruited and semi-structured interviews were completed to explore views about patient interactions and peer relationships on the ward. Thematic analysis produced findings with two main areas of interest. The first area was the type of peer relationship, encompassing the superordinate themes of ‘good relationships’ and ‘negative interactions’. The second area of interest was the factors affecting relationships, which included the ‘environment’ and ‘individual factors’. The research concluded that some peer interactions demonstrated empathy, care and group emotion contagion. External factors, such as the environment, impacted the formation and maintenance of peer relationships, as did internal factors, such as the ability to communicate. The findings identified the need for personal and communal spaces in these settings, with activities off the ward appearing to be beneficial for the formation and maintenance of peer relationships.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.193
GPT teacher head0.420
Teacher spread0.227 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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