Bibliographic record
Abstract
o matter the size of your optometry practice, you have likely experienced human resource issues.It's a fact that we cannot run practices without employees so understanding and preparing for the common challenges is a smart business strategy.Human resources can cover a broad spectrum of topics so let's tackle the top five. #1 HIRING GOOD STAFFIt continues to be the top challenge I hear every time I speak with a practice owner.Finding good staff is hard!We have all hired someone we thought was going to be amazing and then they started calling in sick or bringing negativity into the office.Recruiting well is an art and needs dedicated attention. Solution:Be ready to recruit.Have a job posting created you can update as needed.Have a list of all the places you want to post your job from online jobsites to association job boards and college job fairs.But don't stop there … post it on social media.Have your staff share it on their social.Spread as wide a net as possible so you get a decent size pool of candidates.Now spend the time reviewing each resume and set up interviews.Get your team involved in the hiring so you truly uncover whether the candidate is a
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".