Evidence map›Paper›PMID 29106585›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2018

Development of the PHAST model: generating standard public health services data and evidence for decision-making.

Betty Bekemeier, Seungeun Park

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Betty BekemeierDepartment of Psychosocial and Community Health, University of Washington School of Nursing, Seattle, WA, USA.
Seungeun ParkDepartment of Psychosocial and Community Health, University of Washington School of Nursing, Seattle, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Standardized data regarding the distribution, quality, reach, and variation in public health services provided at the community level and in wide use across states and communities do not exist. This leaves a major gap in our nation's understanding of the value of prevention activities and, in particular, the contributions of our government public health agencies charged with assuring community health promotion and protection. Public health and community leaders, therefore, are eager for accessible and comparable data regarding preventive services that can inform policy decisions about where to invest resources. Methods: We used literature review and a practice-based approach, employing an iterative process to identify factors that facilitate data provision among public health practitioners. Results: This paper describes the model, systematically developed by our research team and with input from practice partners, that guides our process toward maximizing the uptake and integration of these standardized measures into state and local data collection systems. Discussion: The model we developed, using a dissemination and implementation science framework, is intended to foster greater interest in and accountability for data collection around local health department services and to facilitate spatial exploration and statistical analysis of local health department service distribution, change, and performance. Conclusion: Our model is the first of its kind to thoroughly develop a means to guide research and practice in realizing the National Academy of Medicine's recommendation for developing systems to measure and track state and local public health system contributions to population health.

Indexed as

Health Services ResearchInformation DisseminationPublic Health AdministrationData CollectionDatasets as TopicEvidence-Based PracticeGovernment AgenciesModels, TheoreticalPopulation HealthPublic Health Informatics

Identifiers

PMID29106585
PMCPMC7647004

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.