osmanyakubu-maker/online-examination-acceptance-ghana

An anonymized development dataset of 800 Nursing and Midwifery students in Ghana, accompanied by an aligned 40-item questionnaire for examining online examination experience, interface design, system use, learning impact, student opinions and technical challenge Digital Assessment Acceptance Reproducibility Package This archive reproduces the analyses reported in the manuscript “Digital Assessment Acceptance Among Nursing and Midwifery Students in Ghana: A Reproducible Explainable Machine-Learning Study with Independent External Validation.” Contents `run\\\\\\\_analysis.py`: complete executable analysis for psychometrics, model comparison, external validation, bootstrap uncertainty, calibration, threshold sensitivity, SHAP interpretation, tables, figures, predictions, and automated manuscript checks. `data/`: de-identified development (`n=800`) and external-validation (`n=300`) datasets. `instrument/`: the questionnaire corresponding to the deposited variables. `manuscript/`: the unchanged manuscript version used as the reporting reference. `legacy/`: the originally supplied core analysis script retained for provenance. `outputs/`: manuscript tables, figures, predictions, diagnostics, bootstrap results, complete machine-readable results, and the manuscript-match report. `requirements.txt` and `environment.yml`: pinned software environment. `MANUSCRIPT\\\\\\\_RESULTS\\\\\\\_MAP.md`: mapping between manuscript results and package outputs. `checksums.sha256`: SHA-256 integrity hashes for the archive contents. Exact reproduction Python 3.12.13 is required for the manuscript environment. From this directory: Windows ```text python -m venv .venv .venv\\\\\\\\Scripts\\\\\\\\activate python -m pip install --upgrade pip pip install -r requirements.txt run\\\\\\\_analysis.bat ``` macOS or Linux ```text python3.12 -m venv .venv . .venv/bin/activate python -m pip install --upgrade pip pip install -r requirements.txt sh run\\\\\\\_analysis.sh ``` A successful exact run ends with `"status": "PASS"`. The detailed comparison is saved as `outputs/manuscript\\\\\\\_match\\\\\\\_report.json`. Analysis policy needed to match the manuscript The datasets are analyzed exactly as deposited. Categorical predictors are converted to strings, one-hot …