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Record W3214521894 · doi:10.2196/33512

Regional Utilization of Preventive Services in the 55-Plus Age Group: Protocol for a Mixed Methods Study

2021· article· en· W3214521894 on OpenAlexvenueno aff
Ilona Hrudey, Annemarie Minow, Svenja Walter, Stefanie March, Enno Swart, Christoph Stallmann

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Medical Studies
Canadian institutionsnot available
FundersEuropean Regional Development Fund
KeywordsPreventive careGermanMedicineHealth careHealth servicesEnvironmental healthPopulationAge groupsGerontologyPreventive healthcareFamily medicineDemographyPublic healthGeographyNursingPolitical science

Abstract

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BACKGROUND: In Germany, the proportion of people with chronic diseases and multimorbidity is increasing. To counteract the emergence and worsening of age-related conditions, there is a need for preventive care structures and measures. The preventive services that are financed by statutory health insurance (SHI; eg, vaccinations, cancer screening) are only used by part of the German population. There are no current findings about the utilization of these services by older adults in the eastern German federal state of Saxony-Anhalt, which is particularly strongly affected by demographic change. OBJECTIVE: The aim of this study is to investigate the actual utilization and determinants of, reasons for, and barriers to utilization of preventive services financed by the SHI in Saxony-Anhalt in the 55-plus age group. METHODS: In this study, a convergent mixed methods design is used. The actual use of preventive services will be shown by means of (1) a claims data analysis looking at data on statutory outpatient medical care from both the Central Research Institute of Ambulatory Health Care in Germany (Zi) and the Association of Statutory Health Insurance Dentists in Saxony-Anhalt (KZV LSA). The determinants, attitudes, and behaviors associated with use will be analyzed through (2) a cross-sectional survey as well as (3) qualitative data from semistructured interviews with residents of Saxony-Anhalt and from focus group discussions with physicians. (4) A stock take and systematic evaluation of digitally available informational material on colorectal cancer screening, by way of example, provides an insight into the information available as well as its quality. The conceptual framework of the study is the behavioral model of health services use by Andersen et al (last modified in 2014). RESULTS: (1) The Zi and KZV LSA are currently preparing the requested claims data. (2) The survey was carried out from April 2021 to June 2021 in 2 urban and 2 rural municipalities (encompassing a small town and surrounding area) in Saxony-Anhalt. In total, 3665 people were contacted, with a response rate of 25.84% (n=954). (3) For the semistructured interviews, 18 participants from the 4 different study regions were recruited in the same period. A total of 4 general practitioners and 3 medical specialists participated in 2 focus group discussions. (4) For the systematic evaluation of existing informational material on colorectal cancer screening, 37 different informational materials were identified on the websites of 16 health care actors. CONCLUSIONS: This study will provide current and reliable data on the use of preventive services in the 55-plus age group in Saxony-Anhalt. It will yield insights into the determinants, reasons, and barriers associated with their utilization. The results will reveal the potential for preventive measures and enable concrete recommendations for action for the target population of the study. TRIAL REGISTRATION: German Clinical Trials Register DRKS00024059; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00024059. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33512.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.077
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.077
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.046
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0060.006
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0040.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0620.010

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 head0.736
GPT teacher head0.762
Teacher spread0.026 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations6
Published2021
Admission routes1
Has abstractyes

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