The 2024 PhD Board-Certified Clinical Chemist Compensation Survey
Bibliographic record
Abstract
BACKGROUND: Board-certified clinical chemists with doctor of philosophy (PhD) degrees are essential to laboratory medicine and healthcare, yet comprehensive data on their total compensation is limited. The Association for Diagnostics & Laboratory Medicine (ADLM, formerly AACC) Society for Young Clinical Laboratorians Core Committee conducted its fifth compensation survey in April 2024, providing updated data on salary, benefits, and job satisfaction among board-certified clinical chemists. METHODS: The 2024 compensation survey was distributed to all doctoral-level ADLM members based in the United States and Canada (n = 1576). Confidential, self-reported data were collected from respondents on various demographic and professional characteristics, including academic degree, board certification, years of experience, employment sector, total compensation (defined as base salary plus bonus), geographic location, race/ethnicity, and sex. The study included a total of 291 respondents for analysis. RESULTS: Ninety-two percent (n = 267) of the respondents resided in the United States and 90% held a PhD degree. More than half of the respondents were employed in academic, hospital, or healthcare system settings. Among those with board certification, 86% were certified by the American Board of Clinical Chemistry (ABCC). The median total compensation for ABCC board-certified PhD clinical chemists working in the United States (n = 158) was $220 000 to $229 000. CONCLUSIONS: The 2024 survey highlights a continued upward trend in compensation for board-certified PhD clinical chemists, reflecting their expertise and contributions to healthcare. This report serves as a critical resource for professionals to advocate for equitable and competitive compensation as well as benefits that support career advancement and development.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".