The 2018 AACC/SYCL PhD Clinical Chemist Compensation Survey
Bibliographic record
Abstract
BACKGROUND: Doctoral level board-certified clinical chemists play an invaluable role in many facets of laboratory medicine and healthcare. However, information concerning their total compensation is sparse. CONTENT: A confidential self-reported compensation survey was conducted by the American Association for Clinical Chemistry's Society for Young Clinical Laboratorians (AACC SYCL) Core Committee from April 1 to April 17, 2018. Respondents provided information on geographic location, employment sector, gender, and years of experience to account for the influence of these variables on compensation. There were 199 respondents in total from the United States and Canada, however, only respondents employed in the United States with an earned doctoral degree and certification by the American Board of Clinical Chemistry (n = 133), were included in the full analysis. In comparison to compensation reported in AACC SYCL salary surveys conducted in 2010 and 2013, early career median salaries are trending upwards after correction for inflation. SUMMARY: This survey is the first to collect the gender of respondents, and identify a pay gap for some geographic groups. However, this gap could be due in part to a difference in the years of experience, since males were highly represented in the group with >20 years of experience (25 out of 35, 71%). Future studies on compensation trends within clinical chemistry that do not rely on self-report are needed to ensure accuracy and completeness of the dataset.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".