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Record W2769895060 · doi:10.1017/s1463423617000718

Can a customer relationship management program improve recruitment for primary care research studies?

2017· article· en· W2769895060 on OpenAlexafffund
Sharon Johnston, Sabrina T. Wong, Stephanie Blackman, Leena W. Chau, Anne M. Grool, William Hogg

Bibliographic record

VenuePrimary Health Care Research & Development · 2017
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsGF Strong Rehabilitation CentreUniversity of OttawaBC Mental Health & Substance Use ServicesUniversity of British ColumbiaÉlisabeth Bruyère HospitalDalhousie University
FundersCanadian Institutes of Health Research
KeywordsCustomer relationship managementSoftwareComputer scienceProcess managementAutomationKnowledge managementEngineering managementBusinessDatabaseEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Recruiting family physicians into primary care research studies requires researchers to continually manage information coming in, going out, and coming in again. In many research groups, Microsoft Excel and Access are the usual data management tools, but they are very basic and do not support any automation, linking, or reminder systems to manage and integrate recruitment information and processes. OBJECTIVE: We explored whether a commercial customer relationship management (CRM) software program - designed for sales people in businesses to improve customer relations and communications - could be used to make the research recruitment system faster, more effective, and more efficient. FINDINGS: We found that while there was potential for long-term studies, it simply did not adapt effectively enough for our shorter study and recruitment budget. The amount of training required to master the software and our need for ongoing flexible and timely support were greater than the benefit of using CRM software for our study.

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)0.035
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0260.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.001

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 head0.473
GPT teacher head0.623
Teacher spread0.150 · 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 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

Citations1
Published2017
Admission routes2
Has abstractyes

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