Awareness campaigns for cochlear implants: Are we making an impact?
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine if the major public awareness campaign for cochlear implants 'International Cochlear Implant Day' influenced national and international public interest as measured by internet search activity. METHODS: Weekly search volume data in the United States, Canada, Australia, Germany, United Kingdom, Brazil, India, Japan, and a 'Worldwide' group for the search topic 'cochlear implant' was collected from Google Trends over a 5-year period (2017-2021). The 'Campaign' window was defined as 1 week before, the week of, and 2 weeks after International Cochlear Implant Day (February 25th). 'Non-Campaign' weeks were considered any data outside the 'Campaign' window. RESULTS: Of the studied regions, the United States, United Kingdom, Australia, India, and 'Global' demonstrated a significant increase in internet search activity between 2017 and 2021. Although some individual years showed significant increases during the 'Campaign' period for Canada, Germany, Brazil, and Japan, none showed statistically significant increases over the 5-year period studied. CONCLUSION: Public awareness campaigns are recognized crucial elements to delivering effective healthcare, but their success varies worldwide. While data from Google Trends suggests that cochlear implant awareness campaigns can translate into increased internet searches, greater efforts can be made in select countries to improve public interest.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".