Bibliographic record
Abstract
This study targets the period between 2001 and 2006. Physician’s pay statistics relating to all fee for service practitioners in Saskatchewan were collected. Data related to a number of independent variables commonly used in the design of pay policies were also obtained. These variables include physicians’ places of training, availability of local graduates from each specialty program in Saskatchewan, physicians percentage turnover, changes of physician to population ratio in most specialties, average number of physician patient contacts in most specialties and income distribution skewness per specialty. Statistical analysis was carried out using STATA package version 9. Correlations between a number of variables was established. These correlations include Percentage turnover and change in pay per year between 2001 and 2006, Percentage of provincial graduates to total work force per specialty on one hand and change in pay between 2001 and 2006 on the other hand. Change in pay on one hand and change in volume of clinical activities carried out on the other. This paper revealed three relevant findings. Firstly Saskatchewan Health seems to have responded to the shortage of local graduates in certain specialties by allocating these specialties higher pay rise over the last five years. However this increase was associated with increased recruitment of foreign graduates. Secondly there was a consistent, mostly above inflation rate increase in pay for all specialists except clinical pathologists and diagnostic radiologists (two specialties with a relatively high local graduate retention rates). Thirdly, the overall increase in pay negatively correlated with the turnover rates in the case of family physicians, and did not adequately correlate with the changes in work load of the specialists. The writer argues that Saskatchewan Health has to adjust pay modification practices to respond more effectively to graduates recruitment and retention variables. Blaufuss J, Maynard J, Schollars G. Methods of evaluating turnover costs. Nursing Management 1992 (May); 23(5):52-4,56,58-9. Hyde JC, Fottler MD. Determinants of physician vacancy rates in rural hospitals. Journal of Rural Health 1994; 10(1):38-48, 1994. Saskatchewan Health: Annual Financial Report 2006 and Annual Statistical Report 2006.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".