Bibliographic record
Abstract
It was interesting and gratifying to see that optometrists make the list of the top 10 highest-paying, least stressful jobs in the USA. 1 Among health professionals, apparently only orthodontists rate higher; the others in the top five are computer and information systems managers, law professors and physicists.Curiously, professional astronomers are ninth, so even in an alternate universe I might have enjoyed a less stressful career… Stress is just one of the many possible causes of mental illness, and many of our patients and friends probably talk about how much they experience, and how they cope.There has been much in the news media lately about the importance of mental health -Bell Media's annual "Let's Talk" day is a good example of the push in Canadian society to remove the stigma of mental illness and encourage those affected to seek help.As primary care practitioners, we optometrists can also help patients who are facing mental health issues.Our lead article by Drs.DellaBella, Schwartz and Nehmad discusses the efficacy of the PHQ-2 screening tool to identify patients who may benefit from a referral to a mental health specialist for clinical depression.The critical question is, do we have the time, inclination and professional duty to add this screening test to our examination routines, and if so, does it fit with our regulated scope of practice?Our profession has changed immensely over the nearly four decades that I have been practicing.It is not inconceivable that screening for the mental health of our patients will become part of everyday activities.l
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.307 | 0.169 |
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".