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Record W4306912121 · doi:10.1177/11786302221129955

“I Can Sense When My Hands Need Washing”: A Qualitative Study and Thematic Analysis of Factors Affecting Young Adults’ Hand Hygiene

2022· article· en· W4306912121 on OpenAlexaff
Abhinand Thaivalappil, Ian Young, David L. Pearl, Jennifer E. McWhirter, Andrew Papadopoulos

Bibliographic record

VenueEnvironmental Health Insights · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsToronto Metropolitan UniversityUniversity of Guelph
Fundersnot available
KeywordsHygieneThematic analysisHand sanitizerQualitative researchInterpersonal communicationPublic healthYoung adultPsychologyMedicinePersonal hygieneEnvironmental healthApplied psychologyGerontologyNursingFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

Handwashing is one of the most effective and low-cost public health measures. However, it is often not practiced frequently enough or correctly by the public. Young adults in particular have poorer intentions to wash their hands, frequency of handwashing, and sanitizer use compared to other age groups. Therefore, there is a need to identify barriers and facilitators affecting hand hygiene within this group. The objective of this qualitative study was to apply the Theoretical Domains Framework to explore factors which influence hand hygiene among young adults aged 18 to 25 years old. An online questionnaire (n = 37) and thematic analysis were used to generate 3 overarching themes. The main findings indicated internal factors such as knowledge and intentions; interpersonal factors such as social norms; and environmental factors such as reminders, cues, accessibility, and cleanliness of handwashing facilities determined the level of hand hygiene practiced among young adults. The findings suggest that behavior change techniques such as social comparisons and tailored messaging to suit the needs of young adults may be more effective at increasing hand hygiene.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.378
Teacher spread0.328 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations5
Published2022
Admission routes1
Has abstractyes

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