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Enhancing prevention in primary care: are interventions targeted towards consumers’ and providers’ perceived needs?

2000· article· en· W2033319904 on OpenAlexafffundabout
Marie‐Dominique Beaulieu, Yves Talbot, Alejandro R. Jadad, Marianne Xhignesse

Bibliographic record

VenueHealth Expectations · 2000
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversité de MontréalUniversité de Sherbrooke
FundersHealth CanadaMedical Research CouncilCancer Care Ontario
KeywordsRemunerationPsychological interventionFocus groupNursingRealmInformation needsMedicineEvidence-based medicinePurchasingPsychologyMedical educationAlternative medicineMarketingBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore perceived barriers to the implementation of prevention guidelines, with a particular interest to perceived information needs from the point of view of health professionals and consumers. STUDY DESIGN: Focus group. SETTING AND PARTICIPANTS: Eight focus groups were held in three Canadian cities: three with consumer, three with family physician, and two with primary care nurses. ANALYSIS: Inductive analysis based on transcribed interviews. The material was analysed by two of the investigators. Agreement on interpretation was checked independently by three other researchers on 10% of the material. RESULTS: Lack of motivation, discontinuity of care and lack of adequate remuneration were perceived as the strongest barriers to prevention implementation. Computerized information management systems were not perceived by physicians and nurses as strong facilitating factors. Consumers expressed strongly a need for information on non-traditional preventive interventions. Physicians and nurses expressed a need for patient education material more than for practice guidelines. Research evidence was not considered as the first criteria to judge the value of preventive information. CONCLUSIONS: Evidence-based medicine has triggered a massive effort to develop technologies to support the dissemination of evidence-based information on the assumption that poor access to such information is an important barrier to implementation of effective practices. Our results suggest that such an assumption may not be correct. Providing only evidence-based information from the realm of traditional medicine will appear restrictive to most users, particularly to consumers, and may not be as valued as anticipated considering the expressed scepticism toward research evidence.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.152
GPT teacher head0.466
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

Citations11
Published2000
Admission routes3
Has abstractyes

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