What is the level of information technology maturity in Ontario’s long-term care homes? A cross-sectional survey study protocol
Bibliographic record
Abstract
INTRODUCTION: The number of Canadians 75 years and older is expected to double over the next 20 years, putting continuing care systems such as long-term care (LTC) homes under increasing pressure. Health information technology (IT) has been found to improve the quality, safety and efficiency of care in numerous clinical settings and could help optimise LTC for residents. However, the level of health IT adoption in Ontario's LTC homes is unknown and, as a result, requires an accurate assessment to provide a baseline understanding for future planning. METHODS AND ANALYSIS: We will use a cross-sectional design to investigate the level of IT maturity in Ontario's LTC homes. IT maturity will be assessed with the LTC IT Maturity Instrument, a validated survey examining IT capabilities, the extent of IT use and degree of internal/external IT integration across the domains of resident care, clinical support and administrative activities. All LTC homes in Ontario will be invited to participate. The Director of Care for each home will be directly contacted for recruitment. The survey will be distributed online (or by paper, if preferred) to LTC homes and completed by a staff member designated by the LTC to be knowledgeable about its IT systems. Analyses will consist of descriptive statistics characterising IT maturity across LTC homes and inferential statistics to examine the association between key facility-level characteristics (size, ownership, rurality) and IT maturity. ETHICS AND DISSEMINATION: This study was reviewed by the Ottawa Health Science Network Research Ethics Board and was exempt from full ethics review. Findings will be disseminated through peer-reviewed publication and presentations to the scientific community and stakeholders. Dissemination of our findings will not only inform provincial planning for harnessing the potential of technology in LTC but may also enable quality improvement initiatives in individual LTC homes.
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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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".