What do we know about retired physician assistants? A preliminary study
Bibliographic record
Abstract
Retirement generally means the complete end of employment. Retirement is a new phenomenon for physician assistants (PAs), as those trained in the 1970s exit their careers. To better understand retirement patterns of PAs, we undertook a survey in 2011 using a national database. A cadre of 625 respondents met the criteria of being retired and living; the mean age of PA retirement was 61 years (range 47-75 years). Duration of a PA career was 29 years on average (range, 10-40 years). Forty-three percent of respondents retired from family/general medicine and 11% from emergency medicine. Almost all reported receiving Social Security and Medicare; most had some form of a pension. Fewer than one-fifth retired for health reasons. When asked about the timeliness of retiring, 20% wished they had retired later in life; 4% of the men and 7% of the women thought they should have retired earlier; 74% of the men and 73% of the women said they had retired at the right time. Reasons for retiring varied widely. Approximately one-quarter reported volunteering in a medically-related capacity. We suggest that retirement is a concept undergoing evolution in American society and that PAs represent a health profession that reflects the complexity of this evolution.
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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".