MétaCan
Menu
Back to cohort
Record W2181680997 · doi:10.1097/pep.0b013e3181faeb11

Sharing of Lessons Learned From Multisite Research

2010· article· en· W2181680997 on OpenAlexafffund
Alyssa LaForme Fiss, Sarah Westcott McCoy, Doreen J. Bartlett, Lisa A. Chiarello, Robert J. Palisano, Barbara Stoskopf, Lynn Jeffries, Allison Yocum, Audrey Wood

Bibliographic record

VenuePediatric Physical Therapy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster UniversityCanadian Institutes of Health ResearchWestern University
FundersCanadian Institutes of Health Research
KeywordsAnticipation (artificial intelligence)Process (computing)Knowledge translationKnowledge managementMedical educationPsychologyEngineering ethicsMedicineComputer scienceEngineering

Abstract

fetched live from OpenAlex

PURPOSE: To highlight key considerations for planning and implementing multisite research based on experiences and reflections in conducting a large, international, multisite study. DESCRIPTION: Successes and challenges encountered throughout a multisite study process, and collective recommendations for future researchers are presented. Considerations addressed include creation of the research team and a "community of practice," study preparation and management time, approval by institutional review boards, training of future researchers, recruitment and retention of participants, and dissemination and translation of study materials to consumers. IMPORTANCE TO MEMBERS: Multisite research has the potential to create knowledge for pediatric physical therapy through collaboration among knowledgeable researchers and expert practitioners and by increasing the potential for generalization of findings. Effective planning, including anticipation of challenges, is critical to a successful study. Our collective experiences may assist practitioners and researchers in planning, implementing, and completing future multisite studies.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.847
GPT teacher head0.742
Teacher spread0.105 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations17
Published2010
Admission routes2
Has abstractyes

Explore more

Same venuePediatric Physical TherapySame topicHealth Policy Implementation ScienceFrench-language works237,207