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Innovations in Research with Medically Fragile Populations: Using Bulletin Board Focus Groups

2014· article· en· W817363621 on OpenAlexaff
Karen Cook, Susan M. Jack, Harold Siden, Lehana Thabane, Gina Browne

Bibliographic record

VenueThe Qualitative Report · 2014
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of British ColumbiaMcMaster University
Fundersnot available
KeywordsFocus groupPsychologyIntervention (counseling)PopulationMedical educationBulletin boardGerontologyMedicineSociologyComputer science

Abstract

fetched live from OpenAlex

A new group of medically fragile young adults are graduating from pediatric palliative care programs with limited expectations to live beyond early adulthood, and no comparable adult services to support their complex needs. Accessing this population is difficult because of the complexity of their conditions, the extensive personal and equipment supports that limit feasibility for travel, and divergent communication abilities. Therefore, we undertook a descriptive case study using an asynchronous modification of an online focus group, a bulletin board focus group (BBFG). The greatest strengths of the BBFG are the appeal of this methodology for young adults and the multi day focus group becomes both a community and an intervention. An important limitation of this method was participant follow through on discussion threads. This BBFG provided rich and varied types of data, and very positive participant experiences.

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)0.022
metaresearch head score (Gemma)0.006
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 score0.192
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.623
GPT teacher head0.657
Teacher spread0.034 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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