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Record W2059402961 · doi:10.1044/2014_aja-14-0051

Hearing Care for Elders: A Personal Reflection on Participatory Action Learning With Primary Care Providers

2014· article· en· W2059402961 on OpenAlexafffund
Lorienne M. Jenstad, Martha Donnelly

Bibliographic record

VenueAmerican Journal of Audiology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of British Columbia
FundersMinistry of Health, British Columbia
KeywordsParticipatory action researchHearing lossPsychologyHealth careAction (physics)NarrativeAffect (linguistics)MedicineCitizen journalismMedical educationNursingAudiologyCommunicationComputer science

Abstract

fetched live from OpenAlex

PURPOSE: This report is a reflective critical narrative of the authors' experience with an interdisciplinary collaboration of primary care providers (PCPs) and hearing health care providers (HHCPs) that followed the principles of participatory action research/learning. The goal for this report is to describe the 1st author's observed barriers to PCPs' willingness to learn about hearing health care and the subsequent facilitators to learning. METHOD: A team of PCPs and HHCPs created the "Communication: Hearing Loss in Elders" educational module. The module included (a) prereading developed from a combination of literature reviews and contextual factors and (b) a case study. RESULTS: The authors present the list of topics included in the module, and they reflect on why and how these topics were deemed important by the team. In particular, 2 topics were identified for motivating PCPs' behavior regarding hearing health care: (a) the overlap in symptoms and possible causation among depression, dementia, and hearing loss and (b) how hearing can affect efficiency of care regarding other health issues. CONCLUSIONS: This personal reflective narrative gives the authors' opinions about what can make for a successful collaboration and presents some hypotheses regarding information that may motivate behavior change with regard to hearing care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.339
Teacher spread0.276 · 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 designBench or experimental
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

Citations12
Published2014
Admission routes2
Has abstractyes

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