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Record W2050823001 · doi:10.1504/ijbhr.2013.057359

Patients' perceptions of privacy and their outcomes in healthcare

2013· article· en· W2050823001 on OpenAlexaffabout
Natalia Serenko, Lida Fan

Bibliographic record

VenueInternational Journal of Behavioural and Healthcare Research · 2013
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsLakehead University
Fundersnot available
KeywordsConstruct (python library)PerceptionHealth careInformation privacyStructural equation modelingInternet privacyPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is two-fold: 1) to develop a measurement instrument of patient perceptions of privacy in the healthcare sector; 2) to empirically investigate the outcomes of privacy. Privacy is conceptualised as a multi-dimensional construct consisting of three theoretically independent dimensions: informational, physical, and psychological. A survey instrument was developed and subjected to extensive face validity assessment. The model was tested through a survey of 129 healthcare users in Canada by means of partial least squares. The instrument was found to be reliable and valid. Informational privacy is a key component of the overall privacy perceptions of healthcare users, followed by physical privacy. Psychological privacy has no effect on the overall privacy construct. Privacy has a strong effect on trust, which in turn affects the level of commitment, intentions to use the provider’s services in the future, and engagement in positive word-of-mouth.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.199
GPT teacher head0.453
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2013
Admission routes2
Has abstractyes

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