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Record W3184944345 · doi:10.1111/jgs.17379

How old is old? Identifying a chronological age and factors related with the perception of old age

2021· letter· en· W3184944345 on OpenAlexaffabout
Myriam Daignault, Andréanne Wassef, Quôc Dinh Nguyên

Bibliographic record

VenueJournal of the American Geriatrics Society · 2021
Typeletter
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineAge groupsPerceptionDemographyInclusion (mineral)GerontologyMiddle ageYoung adultPediatricsPsychologyInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

There is no consensus on what age delimits “old age” or identifies “older adults.”1 Old age is part of the natural life course and is socially construed.2 The multifaceted nature of the later decades of life may not be reducible to a single numerical age of transition. The elements of elderhood may not align chronologically or be well-bounded. We aimed to identify whether there exists a chronological age delimiting “old age” and investigated factors, for example, sociodemographic and health, influencing the age of transition to being “old.” From September to October 2019, we interviewed 300 participants at the Centre hospitalier universitaire de Montréal (CHUM) with a standardized questionnaire on the age perceived as being “old” and factors influencing this perception. We performed a t-test comparing the age perceived as old by chronological age. We conducted a linear regression of the age perceived as old on intrinsic and sociodemographic characteristics. Content analysis was used to identify and refine reported factors.3 The study was approved by the CHUM's IRB. Inclusion criteria, questionnaire details, and variables can be found in the Supplemental Methods S1. Among the 150 women and 150 men included, the mean age was 58.8 (SD 16.0) years (Table S1). Participants reported 73.7 (SD 10.1, range 45–100) years as the lower bound of “old age.” This age was 70.5 years for participants under 65 and 77.4 years for those aged over 65, a 6.9 years difference (p < 0.001, 95% CI 4.7, 9.2) (Figure S1). Gender, age, self-reported health, and ethnicity were independently associated with the age perceived as old (Table S2). Men perceived old age to be 3.0 years (95% CI −5.2, −0.9) before women. White participants reported old age as occurring 5.4 years (95% CI 1.3, 9.5) later than in non-white participants. There was a gradient for self-reported heath: participants reporting good health perceived old age to be 3.9 years (95% CI −7.6, −0.1) earlier than those with excellent health. The contrast for bad versus excellent health was −5.3 years (95% CI −9.9, −1.0) (Figure 1). Factors influencing the perception of older age focused on: health identified by decline in health (n = 163, 32%), limitations characterized by physical limitations (n = 115, 22%) and decreased independence (n = 57, 11%), and social factors like social convention (n = 29, 6%) and change in physical appearance (n = 25, 5%) (Figure S2). The age transitioning to “old age” was 74 years, but participants reported a range of answers from 45 to 100 years. Intrinsic characteristics influence one's perception of transition to old age. Identifying a single number to demarcate “old age” may be impossible and unwarranted. The older a participant was, the later was the beginning of old age. With compression of morbidity,4 we hypothesize that as one ages, the negative connotations of old age do not come about, or not as early as projected. Another hypothesis is that because “old age” is associated with stigma,5 one progressively repels proximity to it. Health deterioration was the most reported factor in the perception of old age. Worse health status and life satisfaction have been associated with lower subjective age.6 Our findings show that participants who reported better health considered “old age” to be older. Women face negative perceptions regarding aging7; we hypothesize that this may push to postpone “old age.” Greater life expectancy may also explain why their perception of being old occurs later than for men. Perceptions about aging are culturally diverse, and social roles vary.8 Asian, African, and First Nation cultures may show less stigma around aging, which could explain the 5.4-year difference between white and non-white participants. Old age carries many associations and projections, for example, health decline, loss of independence, social isolation.5 We identified that chronological age, health status, gender, and ethnicity form a basis for projections about what it means to be “old.” Of note, these factors overlap with those related to successful aging.9 As “old age” is multifaceted, careful examination of the reasons for demarcating “old age” is required. From a societal perspective, chronological age may be adequate for a standard retirement age. From a medical perspective, age may be useful to identify subgroups for generalizing study results but may be insufficient for decision-making due to heterogeneity in aging.10 Other variables like multimorbidity and disability should be considered. Old age can easily slip into a means for othering and ageism. No single age delimits old age: its perception varies according to chronological age, gender, health status, and ethnicity. The most suitable delineation may vary by context. The purpose of categorizing “old age” should be examined as unjustified delimitation may lead to ageism. All the authors certify that they have no financial, personal, or potential conflict of interest to declare regarding the subject discussed in this manuscript. Myriam Daignault and Quoc Dinh Nguyen developed the project and the main conceptual ideas. Myriam Daignault conducted the interviews, and Quoc Dinh Nguyen analyzed the data. Myriam Daignault and Quoc Dinh Nguyen drafted the manuscript. Myriam Daignault, Quoc Dinh Nguyen, and Andréanne Wassef critically revised the manuscript. There is no sponsor involved in the funding of this project. Table S1: Demographic and health characteristics of the 300 participants included in the study population. Table S2: Linear regression results of old age perceived according to age, gender, race, level of education, and self-reported health. Figure S1: Age perceived as old according to participant age and gender. Figure S2: Relative importance of factors influencing the age of transition to old age. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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 categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
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 head0.047
GPT teacher head0.322
Teacher spread0.275 · 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 designNot applicable
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".

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Citations11
Published2021
Admission routes2
Has abstractyes

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