Information overload in healthcare management: How the READ Portal is helping healthcare managers
Bibliographic record
Abstract
Abstract: Information overload is a serious threat to the productivity of healthcare managers. Instead of facilitating informed decision making, an overabundance of information actually impedes managers from negotiating information effectively. There are many methods of dealing with information overload, one of which is the use of web infomediaries as a source of information. The University of British Colummbia’s Centre for Health Care management’s READ Portal (http://www.read.chcm.ubc.ca) is an example of an infomediary that is striving to help healthcare managers overcome the effects of information overload. This portal aggregates content from numerous high-quality sources, which is then hosted in one easy to access location. Content is condensed into brief abstracts and synopses that are easy to ingest and includes links to full-text articles and papers that viewers can either choose to visit or not, depending on their needs. The READ Portal can be used as a model for other organizations looking to meet the information needs of managers without overwhelming them with excessive information.
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 | 0.034 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".