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Record W2183511853 · doi:10.36510/learnland.v7i2.654

Project Capabilities and Adults With Intellectual Disabilities: Towards Effective Interviewing for a Better Social Participation

2014· article· en· W2183511853 on OpenAlexaffvenue
Stefanie Corona, Christina Hannum, Ann-Louise Davidson

Bibliographic record

VenueLEARNing Landscapes · 2014
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsConcordia University
Fundersnot available
KeywordsInterviewPerspective (graphical)Qualitative researchPsychologyProcess (computing)Medical educationApplied psychologyIntellectual disabilitySemi-structured interviewSociologyComputer scienceMedicineSocial science

Abstract

fetched live from OpenAlex

This practice-based paper presents a series of tips and techniques for interviewing for a better social participation. While we worked on Project Capabilities, we learned valuable lessons we wished to share with practitioners and researchers who work within an inclusive perspective. We argue that qualitative interviewing requires skillful means that are not always an integral part of research preparation, and we share tips and techniques that we developed throughout the process. As we collected the qualitative data through one-on-one interviews, and taught participants to selffilm using iPads, we found that traditional interviewing techniques were too rigid to engage with people living with an intellectual disability. Instead, we used a blend of motivational interviewing and collaborative inquiry techniques, and built rapport with the participants through online social networking. Mobile technologies were also used to build rapport, empowering participants and allowing non-intrusive filming of interviews.

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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.231
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0000.001
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 head0.048
GPT teacher head0.394
Teacher spread0.346 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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