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Record W2802527566 · doi:10.2196/mhealth.9752

Determinants of Intention to Use Mobile Phone Caller Tunes to Promote Voluntary Blood Donation: Cross-Sectional Study

2018· article· en· W2802527566 on OpenAlexvenueno aff
Bernard Appiah, James N. Burdine, Ammar Aftab, Lucy Asamoah‐Akuoko, David Anidaso Anum, Irene Akwo Kretchy, Elfreda Samman, Patience B Appiah, Imelda Bates

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsnot available
Fundersnot available
KeywordsShort Message ServiceDonationPsychological interventionMobile phoneFocus groupPsychologyDescriptive statisticsMedicineConfirmatory factor analysisAdvertisingInternet privacyFamily medicineService (business)NursingBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Voluntary blood donation rates are low in sub-Saharan Africa. Sociobehavioral factors such as a belief that donated blood would be used for performing rituals deter people from donating blood. There is a need for culturally appropriate communication interventions to encourage individuals to donate blood. Health care interventions that use mobile phones have increased in developing countries, although many of them focus on SMS text messaging (short message service, SMS). A unique feature of mobile phones that has so far not been used for aiding blood donation is caller tunes. Caller tunes replace the ringing sound heard by a caller to a mobile phone before the called party answers the call. In African countries such as Ghana, instead of the typical ringing sound, a caller may hear a message or song. Despite the popularity of such caller tunes, there is a lack of empirical studies on their potential use for promoting blood donation. OBJECTIVE: The aim of this study was to use the technology acceptance model to explore the influence of the factors-perceived ease of use, perceived usefulness, attitude, and free of cost-on intentions of blood or nonblood donors to download blood donation-themed caller tunes to promote blood donation, if available. METHODS: A total of 478 blood donors and 477 nonblood donors were purposively sampled for an interviewer-administered questionnaire survey at blood donation sites in Accra, Ghana. Data were analyzed using descriptive statistics, exploratory factor analysis, and confirmatory factory analysis or structural equation modeling, leading to hypothesis testing to examine factors that determine intention to use caller tunes for blood donation among blood or nonblood donors who use or do not use mobile phone caller tunes. RESULTS: Perceived usefulness had a significant effect on intention to use caller tunes among blood donors with caller tunes (beta=.293, P<.001), blood donors without caller tunes (beta=.165, P=.02, nonblood donors with caller tunes (beta=.278, P<.001), and nonblood donors without caller tunes (beta=.164, P=.01). Attitudes had significant effect on intention to use caller tunes among blood donors without caller tunes (beta=.351, P<.001), nonblood donors with caller tunes (beta=.384, P<.001), nonblood donors without caller tunes (beta=.539, P<.001) but not among blood donors with caller tunes (beta=.056, P=.44). The effect of free-of-cost caller tunes on the intention to use for blood donation was statistically significant (beta=.169, P<.001) only in the case of nonblood donors without caller tunes, whereas this path was statistically not significant in other models. CONCLUSIONS: Our results provide empirical evidence for designing caller tunes to promote blood donation in Ghana. The study found that making caller tunes free is particularly relevant for nonblood donors with no caller tunes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.374
Teacher spread0.314 · 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.

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

Citations15
Published2018
Admission routes1
Has abstractyes

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