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Record W2346157036 · doi:10.1016/j.vaccine.2016.04.071

Using behavior change frameworks to improve healthcare worker influenza vaccination rates: A systematic review

2016· review· en· W2346157036 on OpenAlexaff
Kimberly Corace, Jocelyn A. Srigley, Daniel P. Hargadon, Dorothy Yu, Tara K. MacDonald, Leandre R. Fabrigar, Gary Garber

Bibliographic record

VenueVaccine · 2016
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of TorontoQueen's UniversityUniversity of British ColumbiaPublic Health OntarioUniversity of OttawaOttawa HospitalRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsVaccinationPsychological interventionPsycINFOCINAHLHealth belief modelBehavior changeTheory of planned behaviorRisk perceptionMEDLINEHealth careInterpersonal communicationMedicinePsychologyClinical psychologyPerceptionSocial psychologyPublic healthHealth promotionNursingImmunology

Abstract

fetched live from OpenAlex

• We examine the role of behavior change theories in improving healthcare worker (HCW) vaccination rates. • Psychological theories of behavior change successfully predict HCW influenza vaccination uptake. • Behavior change constructs successfully differentiate vaccinated and non-vaccinated HCW. • Attitudes, self-efficacy, perceived risk/benefit, cues to action, and social norms are key constructs. • Behavior change theories may help guide interventions to increase HCW vaccination uptake. Influenza vaccination of healthcare workers (HCW) is important for protecting staff and patients, yet vaccine coverage among HCW remains below recommended targets. Psychological theories of behavior change may help guide interventions to improve vaccine uptake. Our objectives were to: (1) review the effectiveness of interventions based on psychological theories of behavior change to improve HCW influenza vaccination rates, and (2) determine which psychological theories have been used to predict HCW influenza vaccination uptake. MEDLINE, EMBASE, CINAHL, PsycINFO, The Joanna Briggs Institute, SocINDEX, and Cochrane Database of Systematic Reviews were searched for studies that applied psychological theories of behavior change to improve and/or predict influenza vaccination uptake among HCW. The literature search yielded a total of 1810 publications; 10 articles met eligibility criteria. All studies used behavior change theories to predict HCW vaccination behavior; none evaluated interventions based on these theories. The Health Belief Model was the most frequently employed theory to predict influenza vaccination uptake among HCW. The remaining predictive studies employed the Theory of Planned Behavior, the Risk Perception Attitude, and the Triandis Model of Interpersonal Behavior. The behavior change framework constructs were successful in differentiating between vaccinated and non-vaccinated HCW. Key constructs identified included: attitudes regarding the efficacy and safety of influenza vaccination, perceptions of risk and benefit to self and others, self-efficacy, cues to action, and social-professional norms. The behavior change frameworks, along with sociodemographic variables, successfully predicted 85–95% of HCW influenza vaccination uptake. Vaccination is a complex behavior. Our results suggest that psychological theories of behavior change are promising tools to increase HCW influenza vaccination uptake. Future studies are needed to develop and evaluate novel interventions based on behavior change theories, which may help achieve recommended HCW vaccination targets.

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)0.014
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.010
Bibliometrics0.0130.012
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.281
GPT teacher head0.519
Teacher spread0.238 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations176
Published2016
Admission routes1
Has abstractyes

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