Evidence map›Paper›PMID 24158360›Full record

ArticleJournal of genetic counseling2014

Models of service delivery for cancer genetic risk assessment and counseling.

Angela M Trepanier, Dawn C Allain

Abstract read
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In one paragraph

Article in Journal of genetic counseling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–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

46 citing papers in PubMed.

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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.

Angela M TrepanierCenter for Molecular Medicine and Genetics, Wayne State University, 540 E. Canfield Street, 2375 Scott Hall, Detroit, MI, 48201, USA, atrepani@med.wayne.edu.
Dawn C Allain

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing awareness of and the potentially concomitant increasing demand for cancer genetic services is driving the need to explore more efficient models of service delivery. The aims of this study were to determine which service delivery models are most commonly used by genetic counselors, assess how often they are used, compare the efficiency of each model as well as impact on access to services, and investigate the perceived benefits and barriers of each. Full members of the NSGC Familial Cancer Special Interest Group who subscribe to its listserv were invited to participate in a web-based survey. Eligible respondents were asked which of ten defined service delivery models they use and specific questions related to aspects of model use. One-hundred ninety-two of the approximately 450 members of the listserv responded (42.7%); 177 (92.2%) had provided clinical service in the last year and were eligible to complete all sections of the survey. The four direct care models most commonly used were the (traditional) face-to-face pre- and post-test model (92.2%), the face-to-face pretest without face-to-face post-test model (86.5%), the post-test counseling only for complex results model (36.2%), and the post test counseling for all results model (18.3%). Those using the face-to-face pretest only, post-test all, and post-test complex models reported seeing more new patients than when they used the traditional model and these differences were statistically significantly. There were no significant differences in appointment wait times or distances traveled by patients when comparing use of the traditional model to the other three models. Respondents recognize that a benefit of using alternative service delivery models is increased access to services; however, some are concerned that this may affect quality of care.

Indexed as

CounselingGenetic Predisposition to DiseaseAdultFemaleHumansMaleMiddle AgedNeoplasmsRisk AssessmentYoung Adult

Identifiers

What OpenQuestion holds

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Registered trials

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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.