UNDERSTANDING DEMENTIA DISCOURSE DURING ALZHEIMER’S AWARENESS MONTH IN CANADA: FIRST INSIGHTS FROM A TWITTER STUDY
Bibliographic record
Abstract
Abstract Twitter has become a key platform for public health campaigns, ranging from mental health awareness week to diabetes awareness month. However, there is a paucity of knowledge about how Twitter is being used to support health campaigns, particularly for Alzheimer’s Awareness Month. This presentation aims to: 1) identify how Twitter was used to share dementia discourse during Canada’s Alzheimer’s Awareness Month in January; and 2) explore actions to enhance dementia awareness using Twitter for future Alzheimer’s Awareness Month campaigns. Tweets were collected from Twitter using the Twint application in Python from January 1 to January 31, 2022. Filters were used to exclude irrelevant tweets (5,820), and the remaining 1,289 tweets were exported to Excel. Tweets were divided among eleven coders and analyzed using inductive thematic analysis. Analysis revealed four main themes: dementia education and advocacy; fundraising and promotion; sharing experiences of dementia; and opportunities for future actions such as collaborative partnerships and educational tweets to correct stigmatizing language and dementia stereotypes. Increased educational content, collaborative partnerships, and evidence-informed research are essential to enhancing dementia awareness strategies on Twitter during Alzheimer’s Awareness Month in Canada. Further research is needed to develop, implement, and evaluate methods to improve dementia awareness on Twitter during Alzheimer’s Awareness Month and beyond.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".