MétaCan
Menu
Back to cohort

Changes in Self‐Directed Learning Readiness in Dental Students: A Mixed‐Methods Study

2014· article· en· W2330626941 on OpenAlexaffabout
Kalyani Premkumar, Punam Pahwa, Ankona Banerjee, Hitesh Bhatt, Hyun J. Lim

Bibliographic record

VenueJournal of Dental Education · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanSaskatchewan Health Authority
Fundersnot available
KeywordsPsychological interventionMedicinePopulationDrop outPsychologyDental educationFamily medicineDentistryNursingEnvironmental health

Abstract

fetched live from OpenAlex

The purpose of this study was to identify changes in dental students' self-directed learning (SDL) readiness during their education. Guglielmino's SDL readiness scale (SDLRS) was completed at admission by dental students at the University of Saskatchewan and at the end of each year of training. The response rates varied from year to year. Between twenty-seven and thirty students completed the questionnaire each year at admission (93-100 percent of the entering class). The numbers of participants were lower in succeeding years: numbers used for analysis ranged from eleven to twenty-six; years in which fewer than eleven students participated were not included in the analysis. At admission, the students' mean SDLRS score was 228.98 (on a scale from 58 to 290, with 290 the highest); this score was higher than that of the average adult population (214±25.59). There was no significant effect of years of predental education, prior unsuccessful applications to dental school, interview scores, age, or admission test scores. There was a significant drop in SDLRS scores at the end of the first year for most of the cohorts (p<0.001). In addition to the questionnaire part of the study, two instructors and five first- and second-year students participated in focus groups. Those results showed that the individuals defined SDL narrowly and had similar perceptions of curricular factors that affect SDL readiness. The drop in scores one year after admission and lack of change with increased training suggests that current educational interventions may require re-examination and alteration to those that promote self-direction.

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)0.002
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 score0.109
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.430
Teacher spread0.416 · 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

Citations33
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Dental EducationSame topicInnovations in Medical EducationFrench-language works237,207