The historiography of a profession: The societal and political drivers of the health information management profession in Australia
Bibliographic record
Abstract
Health information permeates healthcare delivery from point-of-care, across the continuum of care and throughout the healthcare system’s policy, population health, research, planning and funding arenas. Health information managers (HIMs) expertly manage that information. This commentary theorises the health information management profession for the first time. Its purpose is to identify and contextualise, via a historiographical account, the societal and political drivers that have shaped contemporary Australian health information management and HIMs’ scientific work. It seeks to build our knowledge of the socio-political influences on the profession’s emergence and development, and the projected drivers of its future. Eight critical, socio-political drivers were identified and are addressed in temporaneous order. Scientific medicine has reflected the influences on medicine in the past century and a half of the medical record and other technologies, laboratory-based sciences, evidence-based medicine and evidence-based health. Standardisation has underpinned and guided the profession’s practice. The hegemony of non-medical healthcare managers and resource- and performance-related accountabilities emerged in the 1960s, as did the efficiencies of bureaucratisation in healthcare and post-bureaucratic shifts to textualisation and technogovernance. Technologisation has driven constant change in health information management, as have the forces of the fast-paced risk society. Since the 1980s, the health consumer movement has propelled regulatory mechanisms that accord patients’ access rights to their medical records and mandate information privacy protections. Finally, a nascent commodification of health information has emerged. These forces exert ongoing impacts on the profession. They will, we conclude, singularly and collectively continue to shape its discourses and direction.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".