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Record W2762651107

Psychological Predictors and Consequences of Hearing Health Behaviors in Older Adults

2017· article· en· W2762651107 on OpenAlexaffabout
Manon Marquet, Alison L. Chasteen, Jason E. Plaks, Laksmiina Balasubramanian

Bibliographic record

VenueOpen Repository and Bibliography (University of Liège) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyHearing lossGerontologyDevelopmental psychologyClinical psychologyMedicineAudiology
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Hearing loss is a common chronic condition affecting older adults in Western populations [1, 2]. Even if older adults could benefit from hearing aid use, they are reluctant to wear them [1]. The purpose of the present study was twofold: a) to examine whether older adults’ implicit theories regarding the modifiability of abilities in general would predict their hearing health behaviors; and (b) to assess consequences of these behaviors on subjective aging. Methods: One hundred and sixty-three older adults (Mean age = 68.62, SD = 5.40) completed, among other questionnaires, questions assessing their implicit theories of ability (i.e., seeing general abilities as fixed vs. improvable with effort [3]) and their subjective age (i.e., felt age, do age, look age, and interest age [4]). They also reported their perceptions of the benefits of various hearing health behaviors for slowing the effects of aging (i.e., getting their hearing tested and using hearing aids) as well as their intentions to engage in those behaviors. Results: We found that the more older people considered that abilities are alterable, the more they perceived hearing health behaviors as being beneficial, which in turn increased their intentions to engage in these behaviors. Also, the greater their intentions, the younger subjective age they reported. Key conclusions: These relationships between implicit theories, hearing health behaviors, and subjective age are particularly important to take into account. Indeed, neglecting hearing problems and experiencing an older subjective age may have detrimental health-related outcomes over time [5, 6]. 1. Feder, K., Michaud, D., Ramage-Morin, P., McNamee, J., & Beauregard, Y. (2015). Prevalence of hearing loss among Canadians aged 20 to 79: Audiometric results from the 2012/2013 Canadian Health Measures Survey. Health Reports, 26(7), 18-25. 2. Lin, F. R., Thorpe, R., Gordon-Salant, S., & Ferrucci, L. (2011). Hearing loss prevalence and risk factors among older adults in the United States. The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 66(5), 582-590. doi: doi:10.1093/gerona/glr002 3. Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and development. Philadephia, PA: Psychology Press. 4. Kastenbaum, R., Derbin, V., Sabatini, P., & Artt, S. (1972). “The ages of me”: Toward personal and interpersonal definitions of functional aging. The International Journal of Aging and Human Development, 3(2), 197-211. 5. Bainbridge, K. E., & Wallhagen, M. I. (2014). Hearing loss in an aging American population: Extent, impact, and management. Annual Review of Public Health, 35, 139-152. doi: 10.1146/annurev-publhealth-032013-182510 6. Kotter-Grühn, D., Kornadt, A. E., & Stephan, Y. (2015). Looking beyond chronological age: Current knowledge and future directions in the study of subjective age. Gerontology, 62(1), 86-93. doi: 10.1159/000438671

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
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 head0.070
GPT teacher head0.420
Teacher spread0.350 · 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.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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