Information technology capacities assessment tool in hospitals: Instrument development and validation
Bibliographic record
Abstract
OBJECTIVES: This research integrates existing literature on information technology (IT) in hospitals, and proposes and validates a comprehensive IT capacities assessment tool in these settings. METHODS: A comprehensive literature review was conducted on Medline until September 2006 to identify studies that used specific IT measures in hospitals. The results were mapped and used as a basis for the development of the proposed instrument, which was tested through a survey of Canadian healthcare organizations (N = 221). RESULTS: A total of seventeen studies provided indicators of clinical and administrative IT capacities in hospitals. Based on the mapping of these indicators, a comprehensive IT capacities assessment instrument was developed including thirty-four items exploring computerized processes, thirteen items assessing contemporary technologies, and eleven items investigating internal and external information sharing. A time frame was inserted in the tool to reflect "plans for" versus "current" implementation of IT; in the latter, the extent of current use of computerized processes and technologies was measured on a (1-7) scale. Overall, the survey yielded a total of 106 responses (52.2 percent response rate), and the results demonstrated a good level of reliability and validity of the instrument. CONCLUSIONS: This study unifies existing work in this area, and presents the psychometric properties of an IT capacities assessment tool in hospitals. By developing scores for capturing IT capacities in hospitals, it is possible to further address important research questions related to the determinants and impacts of IT sophistication in these settings.
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 | 0.067 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".