Stereotypes Associated With Age-related Conditions and Assistive Device Use in Canadian Media: Table 1.
Bibliographic record
Abstract
PURPOSE OF THE STUDY: Newspapers are an important source of information. The discourses within the media can influence public attitudes and support or discourage stereotypical portrayals of older individuals. This study critically examined discourses within a Canadian newspaper in terms of stereotypical depictions of age-related health conditions and assistive technology devices (ATDs). DESIGN AND METHODS: Four years (2009-2013) of Globe and Mail articles were searched for terms relevant to the research question. A total of 65 articles were retained, and a critical discourse analysis (CDA) of the texts was conducted. The articles were coded for stereotypes associated with age-related health conditions and ATDs, consequences of the stereotyping, and context (overall setting or background) of the discourse. RESULTS: The primary code list included 4 contexts, 13 stereotypes, and 9 consequences of stereotyping. CDA revealed discourses relating to (a) maintaining autonomy in a stereotypical world, (b) ATDs as obstacles in employment, (c) barriers to help seeking for age-related conditions, and (d) people in power setting the stage for discrimination. IMPLICATIONS: Our findings indicate that discourses in the Canadian media include stereotypes associated with age-related health conditions. Further, depictions of health conditions and ATDs may exacerbate existing stereotypes about older individuals, limit the options available to them, lead to a reduction in help seeking, and lower ATD use. Education about the realities of age-related health changes and ATDs is needed in order to diminish stereotypes and encourage ATD uptake and use.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".