Bibliographic record
Abstract
In their paper, “Measuring Information Technology Investment among Canadian Academic Health Sciences Centres,” one gets the impression that Pederson and Leonard felt a need to share their frustrations. Not only was their task difficult (i.e., trying to get a meaningful grip on how much is being spent on information technology), but the lack of co-operation they encountered must indeed have been exasperating. Having also experienced the “reluctance-to-reveal” phenomenon, this commentator can sympathize with their trials and tribulations. Determining the true cost of information technology (IT) across organizations is indeed a difficult task for a number of reasons. First, as Pederson and Leonard point out, there is little consistency across Canadian healthcare organizations as to what is to be included in the IT domain, let alone the information management (IM) domain. As part of a fourth-year course taught at the University of Victoria in 2004, 28 Chief Information Officers (CIOs) were interviewed by students and asked to describe the departments for which they were responsible. The survey found that the CIOs were heading divisions that had 17 different names, with information management leading the way – used in four sites. To say that our Canadian healthcare CIOs are responsible for a diverse set of departments is an understatement. Areas of responsibility range from the usual information management and technology (IM&T) to others such as networks, health records, decision support, telecommunications, biomedical engineering services, switchboard and information desk, library services and privacy. The areas for which the CIOs were responsible generated a list that was two pages long! Little wonder it is difficult to find a common set of measurements as to what the IT investment really is. A second reason that it is challenging to measure the value of IT investments is because, as Pederson and Leonard point out, “Chief Information Officers (CIOs) and Chief Technology Officers (CTOs) find themselves under increasing pressure to defend the value proposition of IT.” A recent paper by Bend from the Institute of Public Policy Research in England, entitled, “Public Value and eHealth,” puts it even more bluntly: “Despite the clear potential, really solid evidence of a positive impact of IT in practice is still quite scarce.” This is not a conducive climate for measuring and revealing one’s true costs.
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 | 0.080 | 0.256 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".