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Using Informatic Education Strategy to Break Through the Dilemma of Teaching Transplantation in Medical Institutions – Multidisciplinary Medical Team Perspectives

2018· article· en· W2884971010 on OpenAlexaboutno aff
Ming H. Hsieh, Fu- Jong Shih, Shoei‐Shen Wang, Fu‐Jin Shih

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorDilemmaTransplantationTeamworkMedicineHealth careOrgan transplantationMedical educationFamily medicineNursingPsychologyManagementInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Background In Taiwan, the present survival rate after transplant one year are over 80%. However, the organs/ tissues donors per year in Taiwan is too less to resulting in many patients died for no longer waiting. This phenomenon is true in Taiwan, Europe and U.S.A. In the recent five years, data from the "Taiwan Organ Regisry and Sharing Center" shew that although organs/ tissues fundraising had a slight increase, the number of donors does not exhibit responding growth trend. The challenges are identifying appropriate donor, and providing good quality of organ transplantation care among health team members. As such, how to apply informatic technology education for organ transplantation professionals has been suggested. Method This project attempts to apply face-to-face indepth interviews and qualitative content analysis in 4 general hospitals and 3 medical centers. Result Total of 8 interviews were conducted during this period ). The mainly basic information is as follows: 5 female(62.5%), aged 35-45 years with a total average age of 39.88±3.06, 6 married(75.0%). In education, 2 Doctor(25.0%), 3 Master (37.5%), 2 Bachelor(25.0%). In terms of religious belief, 3 Taoism(37.5%), 2 Christian(25.0%), 2 No religious belief (25.0%). In terms of positions, 3 Physisian(37.5%), 5 Nurse(62.5%) include supervisor, nurse practioner, coordinator for each 1, Nursing expert 2, Years of funding ranged from 5 to 30 years (16.8±5.63 years). Intervewees from muldiscipline medical team showed the status and dilemma of the team to perform organ fundraising / donation/ transplantation related work, with 6 core themes. In the relevant education and training situation, the convergence of the 4 core themes. Conclusion There are still many instructional websites in Europe, the United States, Canada and Asia. Most of the related themed courses are illustrated by videos or shared as practical examples, such as pathophysiology or anatomy related topics use the 3D video as an additional textbook to help learners more easily understand and grasp the course content; Advocacy and family support related topics, use the parties to elaborate or share their mentality to help learners witness and recognize in some situations, the actual thoughts, feelings and reactions of the patient/ family member. Overall, the pattern of open participation through a full informatic interactive curriculum was not seen on any websites that were visited. The results discover the current organ fundraising/ donation/ transplantation inadequate education courses, thus fully understand the multidiscipline medical staff’s idea and need of this related issue. It is advisable to follow this study and script theory to develop a theoretical construct to develop complete curriculum framework, and establish a complete fit-in professional and ethics' organ transplantation/ informatic technology learning programs, to increase the inter-disciplinary experience exchange.

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)0.021
metaresearch head score (Gemma)0.014
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: Methods · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.009
Scholarly communication0.0080.007
Open science0.0010.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.396
Teacher spread0.351 · 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
GenreMethods

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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