All We Need to Fear Is Fear Itself: Overcoming the Internet Resistance of Child Psychiatrists
Bibliographic record
Abstract
The Internet is revolutionizing health care, and yet health care providers, doctors in particular, are lagging behind health care consumers when it comes to embracing this new technology. Rather than condemn and dismiss this technophobia as childish and short-sighted, we need to understand its multifactorial origin as well as key strategic elements needed to conquer and eliminate it. This article summarizes lessons learned over a decade of using various approaches toward promoting Internet literacy among child psychiatrists in Toronto, Canada. Building and growing Ability OnLine, an online email network for youngsters with disabilities (1990-1995) was infinitely easier than convincing colleagues to learn about Internet health resources, let alone contribute to a departmental web page. By sharing our successes and failures, we hope to contribute to a FAQ of do's and don'ts for other E-literacy champions in Mental Health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".