Fifty Years of Physical Growth and Impressive Technological Advances Unmatched by Health Human Resources Reform and Cultural Change
Bibliographic record
Abstract
The year 1962 was pre-medicare. The public was concerned about access and individual affordability of care. Funding involved public or private responsibility and the role of government. Physicians, the most influential providers, were concerned that government funding would result in the loss of their independence and their becoming state employees. The retrospective analysis "Looking Back 50 Years in Hospital Administration" by Graham and Sibbald is arresting as it underlines just how much progress we have made in what could be termed "hardware" in support of healthcare policy and hospital administration. From this perspective, the progress has been eye opening, given the advent of universal healthcare, the advancement in our physical facilities, the development of high-quality diagnostic equipment, the explosion of new research centres and new and complex clinical procedures. The development of this hardware has given our providers better weapons and contributed to a remarkable improvement in life expectancy. But progress in health administration and policy management involves more than hardware. If the hardware constitutes the tools, then the "software" of the healthcare system involves the human resources and the culture change that must be positioned to make maximum use of the hardware. In 2062, looking back at the 2012 experience, the legacy test may be whether we dealt with health human resources and culture change at a rate that matched our progress in hardware.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".