User Manuals for a Primary Care Electronic Medical Record System: A Mixed-Methods Study of User- and Vendor-Generated Documents
Why this work is in the frame
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Bibliographic record
Abstract
RESEARCH PROBLEM: Tutorials and user manuals are important forms of impersonal support for using software applications including electronic medical records (EMRs). Differences between user- and vendor documentation may indicate support needs, which are not sufficiently addressed by the official documentation, and reveal new elements that may inform the design of tutorials and user manuals. RESEARCH QUESTION: What are the differences between user-generated tutorials and manuals for an EMR and the official user manual from the software vendor? LITERATURE REVIEW: Effective design of tutorials and user manuals requires careful packaging of information, balance between declarative and procedural texts, an action and task-oriented approach, support for error recognition and recovery, and effective use of visual elements. No previous research compared these elements between formal and informal documents. METHODOLOGY: We conducted an mixed methods study. Seven tutorials and two manuals for an EMR were collected from three family health teams and compared with the official user manual from the software vendor. Documents were qualitatively analyzed using a framework analysis approach in relation to the principles of technical documentation described above. Subsets of the data were quantitatively analyzed using cross-tabulation to compare the types of error information and visual cues in screen captures between user- and vendor-generated manuals. RESULTS AND DISCUSSION: The user-developed tutorials and manuals differed from the vendor-developed manual in that they contained mostly procedural and not declarative information; were customized to the specific workflow, user roles, and patient characteristics; contained more error information related to work processes than to software usage; and used explicit visual cues on screen captures to help users identify window elements. These findings imply that to support EMR implementation, tutorials and manuals need to be customized and adapted to specific organizational contexts and workflows. The main limitation of the study is its generalizability. Future research should address this limitation and may explore alternative approaches to software documentation, such as modular manuals or participatory design.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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