Bibliographic record
Abstract
The proportion of the US population over age 65 is projected to reach almost 80 million by the year 2040, doubling the numbers from 2000 (Administration on Aging, 2012). With the aging of the population, the incidence of age-related diseases and disorders like stroke and dementia is expected to increase, adding to the caseloads of speech-language pathologists (SLPs). Most SLPs, by contrast, are younger adults; over a quarter of SLPs in the US are under age 35 (ASHA, 2012). Thus, as the elderly population grows, more intergenerational communication encounters will occur between SLPs and their aging clients, increasing demands for cultural competence, specifically with regard to ageism. However, the field of speech-language pathology has seen little research into the impact of age-related stereotypes on service delivery (Armstrong & McKechnie, 2003). One’s interactions with people are implicitly shaped by stereotypes, widely held unconscious representations of groups of people (Devine, 1989). According to the Age Stereotypes in Interaction model (Hummert, 2012), there are three main factors that trigger stereotypes: the perceiver’s self-system, the context of the interaction, and physical traits. ‘Self-system’ refers to one’s beliefs and attitudes, which are themselves determined by one’s age, cognitive complexity, and past experiences (Hummert, 2012; Ryan, 2007). Stereotypes can be reinforced by the context in which intergenerational encounters occur. To illustrate, Hummert and colleagues (1998) found that younger adults used different language when speaking to older adults in the hospital vs an apartment. Aspects of physical appearance (e.g. grey hair, stooped posture) create an immediate impression of the older individual (Adams et al., 2012). Using photographs, Hummert and colleagues (1997) found that adults perceived to be older were stereotyped more negatively than younger-looking adults. Negative stereotypes may, in turn, affect older adult’s responses, resulting in a cycle of reinforced stereotypes and negative interactions (Ryan, 2007). Williams and colleagues (2009) found that nurses who used ‘elderspeak’ met with more resistance to care in their patients with dementia. To prevent such negative interactions, SLPs must become aware of the potential impact of implicit age-related stereotypes. The purpose of this study was to determine whether SLP students are influenced by age-related stereotypes when judging the communication of older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".