The Response of Mobile Applications to Crisis in Canada
Bibliographic record
Abstract
When a crisis strikes, technology allows information to move quickly. By leveraging mobile technology, mobile applications (apps) can serve as a reliable way to ensure rapid communication. This thesis evaluated mobile apps created for two crises: the COVID-19 pandemic and the Indigenous Mental Health crisis. Through the completion of two independent scoping reviews on each topic, apps were collected, analyzed and assessed in a double-blind nature, including results from both grey and scientific literature searches. The results of these scoping reviews were compiled to create an overall report on the capability of these apps to address each crisis respectively. The results indicate that while apps can be quickly developed and made available on application stores in order to help mount a rapid response to crises, many do not fit the needs of users and none can completely cater to a crisis within one app. Further research is required to provide evidence of effectiveness, acceptability and usabilityof these apps. Innovation and collaboration between key stakeholders, government, health care organizations and application developers will be essential to address the identified gaps and facilitate the creation of successful apps for use in either crisis.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".