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Record W2485902563 · doi:10.1017/s1041610216000144

Peer volunteer perspectives following a complex social cognitive intervention: a qualitative investigation

2016· article· en· W2485902563 on OpenAlexaff
O. Lee, Niamh Aspell, L. Connolly, Brian Lawlor, Sabina Brennan

Bibliographic record

VenueInternational Psychogeriatrics · 2016
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsTrinity College
Fundersnot available
KeywordsPsychological interventionThematic analysisIntervention (counseling)PsychologyFocus groupCognitionSocial cognitive theoryQualitative researchPeer groupClinical psychologyGerontologyMedicineDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Peer volunteers can be key to delivering effective social cognitive interventions due to increased potential for social modeling. We consulted peer volunteers who had just taken part in an 8-week social and nutritional mealtime intervention with older adults living alone, to seek their evaluation of the intervention. METHODS: Semi-structured focus groups were used with a total of 21 volunteers (17 female) and two facilitators. Thematic analysis was used to interrogate the data. RESULTS: Six themes (16 sub-themes) are discussed. Peer volunteers described the importance of the socializing aspect of the intervention, of pairing considerations and compatibility in peer interventions, of considering the needs of the participant, of benefits to the volunteers, and of the practical considerations of conducting an intervention. Volunteers also discussed considerations for future research and services for older adults living alone. CONCLUSIONS: Volunteers found their involvement in the intervention to be personally beneficial, and revealed some valuable considerations for the researchers to take forward to future research. Results are pertinent to intervention design and could inform future social cognitive and other peer-oriented interventions for older adults living alone.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.157
GPT teacher head0.528
Teacher spread0.371 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations10
Published2016
Admission routes1
Has abstractyes

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