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Record W2025314120 · doi:10.1080/02687038.2015.1006564

“Who decides what criteria are important to consider in exploring the outcomes of conversation approaches? A participatory health research study”

2015· article· en· W2025314120 on OpenAlexfundno aff
Ruth McMenamin, Edel Tierney, Anne Mac Farlane

Bibliographic record

VenueAphasiology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le Cancer
KeywordsConversationPsychologyThematic analysisParticipatory action researchStakeholderBrainstormingFocus groupAphasiaMedical educationCitizen journalismConversation analysisCompetence (human resources)Applied psychologyQualitative researchPublic relationsMedicineComputer scienceSocial psychologySociologyPolitical science

Abstract

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Background: One of the most devastating consequences of aphasia is the disruption to normal conversation. The Conversation Partner Programme emphasises communicative competence and life participation. Currently there is no recognised system for evaluating this intervention. Following policy imperatives for patient and public involvement, it is important to include service users in the development of evaluation criteria. However, people with aphasia are often excluded from such research and service development initiatives because of their communication disability. This study was designed to include people with aphasia and other key stakeholders as co-researchers in the development of evaluation criteria for a Conversation Partner Programme.Aims: To describe the multi-perspectival co-generation of Conversation Partner Programme evaluation criteria using a participatory research approach.Methods & Procedures: Following a pilot study, the generation and analysis of qualitative data involved a Participatory Learning and Action (PLA) approach based on the interpretive paradigm. Using purposeful sampling participants (n = 20) included: people with aphasia (n = 5); speech and language therapists (n = 5); speech and language therapy graduates and undergraduates (n = 9) and university coordinator (n = 1). Through (n = 18) individual and inter-stakeholder data generation episodes (PLA focus groups and interviews) using participatory techniques (Flexible Brainstorming, Card Sort, Direct Ranking, Seasonal Calendar), evaluation criteria were identified. The principles of thematic analysis guided the co-analysis of data with participants. Data generated in Ireland were presented to an international inter-stakeholder group at Connect, UK, for preliminary exploration of transferability of findings.Outcomes & Results: Conversation Partner Programme evaluation criteria agreed and prioritised by co-researchers in order of importance included: (1) shared understanding of structure, (2) clarity about the programme, (3) agreed evaluation mechanism, (4) linking with other organisations, and (5) feedback. “Shared Understanding of Structure” was ranked the most important criterion and related to the nature and number of participants, opportunities for group meetings, socialising, and stakeholder interaction. “Feedback”, the criterion ranked least important, detailed responsibilities about summarising programme experiences and sharing this information between stakeholders.Conclusions: People with aphasia and other key stakeholders were meaningfully involved in the identification of evaluation criteria for a Conversation Partner Programme. The outcomes of this collaborative work bridge the gap between policy imperatives around involvement and actual practice and will impact the design, delivery, and evaluation of the programme for all stakeholders. Findings will be of interest to professionals in this clinical area and to those exploring innovative methodologies to include marginalised service users, especially people with communication disabilities in research.

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)0.280
metaresearch head score (Gemma)0.219
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.888

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2800.219
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0150.023
Scholarly communication0.0130.015
Open science0.0030.011
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.768
GPT teacher head0.504
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations30
Published2015
Admission routes1
Has abstractyes

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