An Application of the Hospital-in-the-Home Unlearning Context
Bibliographic record
Abstract
Many researchers who have investigated health care organizations have indicated that health care professionals are replete with outdated knowledge, and some researchers go even further to argue that without the presence of a context that facilitates unlearning (forgetting) practitioners may lose the ability to recognize relevant changes with respect to knowledge pertaining to all aspects of the health care sector and they may decide to rely on potentially out-of-date knowledge and inappropriate ways of interpreting data with attendant loss of decision quality and attendant risks. This article presents an analysis and develops a model of the factors that influence unlearning which is focused on the health care industry and is comprised of three constituent components: (1) a framework characterizing the lens through which individuals view situations; (2) a framework for characterizing how individual habits change; and (3) a framework for characterizing the manner in which emergent understandings are consolidated into existing knowledge and knowledge structures. The model was developed and analyzed using qualitative data from the Hospital-in-the-Home Unit of a Spanish Regional Hospital. From a practical perspective the article provides for the identification of factors that influence the nature and effectiveness of the unlearning context in Hospital-in-the-Home-Units in regional hospitals. This not only valuably adds to the knowledge of the way these units function but also may enable actions to be taken to improve the learning processes associated with such units, resulting in an improvement in the quality of knowledge used in day-to-day decision making. It is to be assumed that, as a result of improving the quality of knowledge used in decision making, the quality of decisions will be improved.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".