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Record W1725240753 · doi:10.25011/cim.v30i4.2835

74. Remediation plans: Effectively matching trainee needs to remediation planss

2007· article· en· W1725240753 on OpenAlexvenueno aff
Susan Glover Takahashi, Dawn Martin, Sunil Verma, Sheri Edwards

Bibliographic record

VenueClinical and investigative medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental remediationPlan (archaeology)Remedial educationProcess (computing)Intervention (counseling)Process managementMedical educationEngineering managementEngineeringComputer sciencePsychologyMedicineNursing

Abstract

fetched live from OpenAlex

This paper is a retrospective study reporting on the development of remediation plans for residents who are having difficulty meeting the established program goals and objectives. Additionally, the paper describes the implementation of a consistent, competency focused approach to remediation using a standardized needs assessment and intervention planning tool has functioned to better manage difficulty. First, the paper provides a profile of the educational needs of 20 recent cases describing their specialty programs, training levels and the competency areas of difficulty. Next the paper outlines an educational inquiry tool used by residency program directors to develop, implement and evaluate the trainee’s remediation programs. The tool includes inquiry questions which the faculty answer in the development of a customized remedial educational plan in the such areas as: trainee background, trainee information, overall rationale for remediation plan, training profile, purpose of remediation, details of remediation plan, anticipated outcome of remediation plan, other factors impacting trainee success. The tool is designed to be reviewed with trainee input to ensure the desired outcomes and process for the remediation plan are transparent for both the trainee and program director. Finally three case studies are described in detail including of the types of problems that lead to remediation, examples of the remediation plans developed and the range of approaches employed to support the success of residents. The paper then summarizes the identified key issues and options in optimizing success for residents in difficulty. Christopher I, Doty CI, Lucchesi M. The Value of a Web-based Testing System to Identify Residents Who Need Early Remediation: What Were We Waiting For? Acad Emerg Med 11(3):324. Beeson MS, Jwayyed S. Development of a Specialty-wide Web-based Medical Knowledge Assessment Tool for Resident Education. Acad. Emerg. Med 2004 (Mar); 11(3):324. Boiselle PM. Remedy for Resident Evaluation and Remediation, Academic Radiology 2005(July); 12(7):894-900.

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)0.009
metaresearch head score (Gemma)0.041
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.164
GPT teacher head0.402
Teacher spread0.238 · 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
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

Citations0
Published2007
Admission routes1
Has abstractyes

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