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Record W2729779815 · doi:10.2196/resprot.6859

The Feasibility and Acceptability of a Web-Based Alcohol Management Intervention in Community Sports Clubs: A Cross-Sectional Study

2017· article· en· W2729779815 on OpenAlexvenueno aff
Tameka McFadyen, Luke Wolfenden, John Wiggers, Jennifer Tindall, Sze Lin Yoong, Christophe Lecathelinais, Karen Gillham, Shauna Sherker, Bosco Rowland, N McLaren, Melanie Kingsland

Bibliographic record

VenueJMIR Research Protocols · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHarmAlcoholIntervention (counseling)Web applicationApplied psychologyMedicinePsychologyComputer scienceNursingWorld Wide WebSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The implementation of comprehensive alcohol management strategies can reduce excessive alcohol use and reduce the risk of alcohol-related harm at sporting venues. Supporting sports venues to implement alcohol management strategies via the Web may represent an effective and efficient means of reducing harm caused by alcohol in this setting. However, the feasibility and acceptability of such an approach is unknown. OBJECTIVE: This study aimed to identify (1) the current access to and use of the Web and electronic devices by sports clubs; (2) the perceived usefulness, ease of use, and intention to use a Web-based program to support implementation of alcohol management policies in sports clubs; (3) the factors associated with intention to use such a Web-based support program; and (4) the specific features of such a program that sports clubs would find useful. METHODS: A cross-sectional survey was conducted with club administrators of community football clubs in the state of New South Wales, Australia. Perceived usefulness, ease of use and intention to use a hypothetical Web-based alcohol management support program was assessed using the validated Technology Acceptance Model (TAM) instrument. Associations between intention to use a Web-based program and club characteristics as well as perceived ease of use and usefulness was tested using Fisher's exact test and represented using relative risk (RR) for high intention to use the program. RESULTS: Of the 73 football clubs that were approached to participate in the study, 63 consented to participate and 46 were eligible and completed the survey. All participants reported having access to the Web and 98% reported current use of electronic devices (eg, computers, iPads/tablets, smartphones, laptops, televisions, and smartboards). Mean scores (out of a possible 7) for the TAM constructs were high for intention to use (mean 6.25, SD 0.87), perceived ease of use (mean 6.00, SD 0.99), and perceived usefulness (mean 6.17, SD 0.85). Intention to use the Web-based alcohol management program was significantly associated with perceived ease of use (P=.02, RR 1.4, CI 1.0-2.9), perceived usefulness (P=.03, RR 1.5, CI 1.0-6.8) and club size (P=.02, RR 0.8, CI 0.5-0.9). The most useful features of such a program included the perceived ability to complete program requirements within users' own time, complete program accreditation assessment and monitoring online, develop tailored action plans, and receive email reminders and prompts to complete action. CONCLUSIONS: A Web-based alcohol management approach to support sports clubs in the implementation of recommended alcohol management policies appears both feasible and acceptable. Future research should aim to determine if such intended use leads to actual use and club implementation of alcohol management policies.

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)0.012
metaresearch head score (Gemma)0.020
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 score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.385
GPT teacher head0.596
Teacher spread0.210 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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