Evidence map›Paper›PMID 36290137›Full record

ArticleAnimals : an open access journal from MDPI2022

Using Latent Profile Analysis to Evaluate Preferences for Chronic Wasting Disease Management Options among Different Hunter Types.

Elena C Rubino, Christopher Serenari

Open access · goldAbstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 42% of its field
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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 1 country.

Elena C RubinoCollege of Forestry, Agriculture and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71656, USA.ORCID 0000-0001-8037-8385
Christopher SerenariDepartment of Biology, Texas State University, San Marcos, TX 78666, USA.
Texas State University · USUniversity of Arkansas at Monticello · US

Funding

Texas Parks and Wildlife Department na
6 · The paper itself

Abstract

Wildlife agencies seek to understand how hunters have and will respond to chronic wasting disease (CWD) management policies because of the vital role hunters play in deer management efforts. As such, dozens of studies have examined the human dimensions of CWD management and policy to assess stakeholder support for management alternatives and reveal what drives support. However, most of these studies have not (1) fully explored the heterogeneity that exists among hunters, and (2) accounted for the tradeoffs that agencies and hunters must make with regard to deer management and potential CWD policy alternatives. To address these deficiencies, we used latent profile analysis to create different typologies of hunters based on a survey of Texas hunters, then analyzed discrete choice experiments investigating the CWD management preferences of these typologies. Across five hunter typologies, we found strong overall support for CWD management, although attitudes towards Texas Parks and Wildlife Department were variable. Preferences for CWD management policies greatly differed between each hunter typology. Wildlife agencies can refer to our findings to better develop hunter-preferred CWD management policies and identify areas of compromise between typologies. Our results also provide agencies with insights regarding how to better communicate about CWD management with different types of hunters.

Indexed as

CWDdeer managementdiscrete choice experimenthunter typology

Identifiers

PMID36290137
PMCPMC9597740
OpenAlexW4306149886

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.