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Record W2946354733 · doi:10.1111/hir.12257

Evaluating user experiences of a clearing house for health policy and systems

2019· article· en· W2946354733 on OpenAlexaff
Boniface Mutatina, Robert Basaza, Nelson K. Sewankambo, John N. Lavis

Bibliographic record

VenueHealth Information & Libraries Journal · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsCredibilityClearingInformation systemResource (disambiguation)Psychological interventionComputer scienceInformation accessHealth policyKnowledge managementPublic relationsMedicineBusinessWorld Wide WebPublic healthNursingPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Timely access to evidence increases the prospects for evidence informed decision making. We evaluated user experiences of a Clearing House for Health Policy with the aim of increasing access to evidence about the Uganda health system and interventions. METHODS: We conducted in-depth interviews with 15 potential users including policymakers, health policy advisors, health managers and researchers to provide evidence on their experience with the clearinghouse. On average participants took 20 minutes to first navigate the site and 45 minutes to perform search tasks and complete the interview. RESULTS: Most respondents successfully searched for information with accuracy and completeness in a short time. Participants commended the performance and expressed high regard for the credibility of the clearinghouse. The majority felt that using the resource was worth their effort. The clearinghouse provided appropriate functionalities for information searching. Navigating and finding information from the site was achievable. However, inadequate background information about the site and lack of current information were widely reported. CONCLUSION: Our paper provides insights on the issues that can be addressed to improve online resources for health policy and system information in a limited resource setting. Users' experience of such resources can be improved by regularly appraising and appropriately indexing the contents, and optimising the capacity to customise information.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.005
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 head0.527
GPT teacher head0.647
Teacher spread0.120 · 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
Published2019
Admission routes1
Has abstractyes

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