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Record W1484338393 · doi:10.21225/d5rk5p

A Comparison of Two Methods of Needs Assessment: Implications for Continuing Professional Education

2002· article· en· W1484338393 on OpenAlexaffvenueabout
Michiko Igarashi, Linda G. Suveges, Gwenna Moss

Bibliographic record

VenueCanadian Journal of University Continuing Education · 2002
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNeeds assessmentContinuing educationMedical educationContinuing professional developmentIdentification (biology)Needs analysisProfessional developmentInformation needsPsychologyProfessional associationSpecial needsMedicineContinuing medical educationPublic relationsSociologyPolitical scienceComputer scienceMathematics education

Abstract

fetched live from OpenAlex

Needs identification is an important component of program planning in continuing professional education. Learners, professional associations, and society all have a stake in ensuring that programs are relevant and focused on important educational needs of professionals. This study compared two different methods of identifying learning needs--perceived needs and knowledge-based needs--for a group of practicing pharmacists (N= 113). The Canadian Consensus Asthma Management Guidelines (1996) provided the framework for the needs assessments and the standard against which pharmacists' knowledge of asthma treatment was assessed. Using data collected via a questionnaire, rank correlation tests showed no relationship between perceived needs and knowledge-based needs. While there was correspondence between the two methods on a few items, overall they did not identify the same needs. This confirmed the results of other research that there are some educational needs of which learners are unaware. Even with the limitations of perceived needs, few continuing professional educators would advocate abandoning this method, although most advocate a combination of methods. The following article discusses the implications of these and other research findings, and current literature on needs assessment in continuing professional education. Many questions remain, however, and there is a need for more research on needs assessment in continuing professional education.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.172
GPT teacher head0.528
Teacher spread0.356 · 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

Citations9
Published2002
Admission routes3
Has abstractyes

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