How Can We Develop Contextualized Theories of Effective Use? A Demonstration in the Context of Community-Care Electronic Health Records
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We contribute to the shifting discourse in the literature on information system use, towards context-specific (rather than general) theories and effective use (rather than just use). Organizations are under great pressure to use information systems effectively but they have few theories to turn to for insights. Motivated by this need, we propose an approach for developing context-specific theories of effective use. The approach suggests that effective use can be theorized by: (1) understanding how a network of affordances supports the achievement of organizational goals, (2) understanding how the affordances are actualized, and (3) using inductive theorizing to elaborate these principles in a given context. We demonstrate the approach in the context of a Canadian health authority’s use of a community-care electronic healthcare record (EHR). We discovered that effective use in this context can be viewed at a high level as the accuracy and consistency with which users work with the EHR, and how they engage in reflection-in-action across a network of nine affordances. The key, however, is understanding how those elements interact with the multiple levels of data needed to achieve the organization’s various goals. Overall, we contribute by offering an approach for developing context-specific theories of effective use, demonstrating its usefulness in an important context, and discovering the importance of understanding in a new way the multilevel nature of information systems. The online appendix is available at https://doi.org/10.1287/isre.2017.0702 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it