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Record W2020947353 · doi:10.2196/jmir.1260

Perspectives of Family Physicians on Computer-assisted Health-risk Assessments

2010· article· en· W2020947353 on OpenAlexafffundabout
Farah Ahmad, Harvey A. Skinner, Donna E. Stewart, Wendy Levinson

Bibliographic record

VenueJournal of Medical Internet Research · 2010
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity Health NetworkYork UniversityPublic Health OntarioUniversity of Toronto
FundersInstitute of Gender and HealthSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsMedicineFamily medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: The firsthand experience of physicians using computer-assisted health-risk assessment is salient for designing practical eHealth solutions. OBJECTIVE: The aim of this study was to enhance understanding about computer-assisted health-risk assessments from physicians' perspectives after completion of a trial at a Canadian, urban, multi-doctor, hospital-affiliated family practice clinic. METHODS: A qualitative approach of face-to-face, in-depth, semi-structured interviews was used. All interviews were audio recorded and field notes taken. Analytic induction and constant comparative techniques were used for coding and analyses. Interpretation was facilitated by peer audit and insights gained from the social exchange theoretical perspective. RESULTS: Ten physicians (seven female and three male) participated in the interviews. Three overarching themes emerged in relation to computer-assisted health-risk assessments: (1) perceived benefits, (2) perceived concerns or challenges, and (3) feasibility. Physicians unanimously acknowledged the potential of computer-assisted health-risk assessments to open dialogue on psychosocial health risks. They also appreciated the general facilitative roles of the tool, such as improving time-efficiency by asking questions on health risks prior to the consultation and triggering patients' self-reflections on the risks. However, in the context of ongoing physician-patient relationships, physicians expressed concerns about the impact of the computer-assisted health-risk assessment tool on visit time, patient readiness to talk about psychosocial issues when the purpose of the visit was different, and the suitability of such risk assessment for all visits to detect new risk information. In terms of feasibility, physicians displayed general acceptance of the risk assessment tool but considered it most feasible for periodic health exams and follow-up visits based on their perceived concerns or challenges and the resources needed to implement such programs. These included clinic level (staff training, space, confidentiality) and organizational level (time, commitment and finances) support. CONCLUSIONS: Participants perceived computer-assisted health-risk assessment as a useful tool in family practice, particularly for identifying psychosocial issues. Physicians displayed a general acceptance of the computer tool and indicated its greater feasibility for periodic health exams and follow-up visits than all visits. Future physician training on psychosocial issues should address physicians' concerns by emphasizing the varying forms of "clinical success" for the management of chronic psychosocial issues. Future research is needed to examine the best ways to implement this program in diverse clinical settings and patient populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT00385034; http://clinicaltrials.gov/ct2/show/NCT00385034 (Archived by WebCite at http://www.webcitation.org/5pV8AGRgt).

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)0.028
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.013
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 head0.180
GPT teacher head0.599
Teacher spread0.419 · 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 designNot applicable
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

Citations30
Published2010
Admission routes3
Has abstractyes

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