Factors influencing healthcare consumers' search for healthcare associated infection information on the World Wide Web
Bibliographic record
Abstract
Purpose This paper seeks to provide a narrative review of some of the factors that influence healthcare consumers' information seeking involving healthcare associated infections (HAI) on the internet. Design/methodology/approach The paper takes the form of a narrative review arising from the authors' presentation and subsequent discussions that took place during the Universities Council Symposium held in Vancouver, Canada in May 2011. Findings There are a number of important factors that affect healthcare consumers' desire to seek information online about HAI, including the search engine used, the type of technology used, web site usability, information availability, consumers' learning style, consumers' personality traits, and finally, consumers' situational, emotional, and psychological contexts. These factors may affect healthcare consumers' decision making about where they will obtain healthcare (i.e. in their selection of a clinic, hospital, regional health authority and/or health care system). Research limitations/implications HAI reporting via web sites is being done by health care organizations across North America. There is a need to more fully understand the factors that affect consumer use of these web sites. Practical implications Fundamental questions have been raised about the impact of providing HAI information over the WWW. There is a need to consider the varying factors that influence consumers' information seeking involving the WWW (i.e. technology‐driven and consumer‐driven factors) especially when searching for HAI‐related information about health care organizations. Originality/value Historically, HAI information was the purview of those who had a background to interpret such data (e.g. infection control and public health practitioners). The literature focusing on what consumers want to know regarding HAIs over the WWW is only beginning to emerge. More research is needed to better understand what health care consumers need to support their decision making involving HAIs.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".