A framework for information systems evaluation: the case of an integrated community‐based health services delivery system
Bibliographic record
Abstract
Information Systems (IS) theory concentrates on getting the right information at the right time in the right format to the right user. The development of information systems, then, requires focus on organizational objectives, designs and dynamics as much as it requires focus on the procurement of the most appropriate hardware and software. The essence of “systems analysis” should not focus on computer‐related concerns, but rather focus on the root of the problem which is the need for the right information. Moreover, not only should this analysis focus on the functionality of the organization but also on the improved effectiveness derived from the new or upgraded information system. In this paper, we present information ‐ in the form of outcome measures ‐ which are needed to initiate, and subsequently evaluate health delivery performance within Integrated Community‐Based Health Delivery Systems.
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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 | 0.085 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".