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Record W3196938544 · doi:10.1177/2333794x211043061

Exploring Health Professionals’ Experiences With a Virtual Learning and Mentoring Program (Project ECHO) on Pediatric Palliative Care in South Asia

2021· article· en· W3196938544 on OpenAlexaff
Megan Doherty, Shokoufeh Modanloo, Emily Evans, Jennifer Rowe, Dennis Newhook, Gayatri Palat, Douglas Archibald

Bibliographic record

VenueGlobal Pediatric Health · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsChildren's Hospital of Eastern OntarioBruyèreUniversity of Ottawa
Fundersnot available
KeywordsPalliative careMedicineFocus groupEcho (communications protocol)Medical educationHealth professionalsHealth careNursingComputer scienceSociology

Abstract

fetched live from OpenAlex

Project ECHO (Extension of Community Healthcare Outcomes) is an innovative model of online education which has been proposed to enhance access to palliative care in resource-limited settings. There is limited literature describing how health care providers in low-and middle-income countries benefit from and learn from this type of training. This qualitative description study explores the learning experiences of participants in a Project ECHO program on pediatric palliative care in South Asia through focus group discussions. Discussions were transcribed, coded, independently verified, and arranged into overarching themes. We identified learning themes including the importance of creating a supportive learning community; the opportunity to share ideas and experiences; gaining knowledge and skills, and access to additional learning materials. Designing future programs to ensure a supportive and interactive learning community with attention cultural challenges may enhance learning from future Project ECHO programs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.409
Teacher spread0.317 · 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 designQualitative
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

Citations20
Published2021
Admission routes1
Has abstractyes

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