Attrition to completion progression theory in an associate degree nursing program
Bibliographic record
Abstract
Objective: This study aimed to develop a substantive theory on the process of overcoming attrition among returning students in an Associate Degree Nursing program in a private school in Arizona.Methods: A qualitative design was utilized for this study. The Classical Grounded Theory approach provided a systematic process of data collection, analysis and synthesis that led to the emergence of the Attrition to Completion Progression Theory. Theoretical sampling through in-depth interviews of ten participants who experienced temporary attrition and was successfully able to come back to succeed within the program provided a rich source of data and unique perspective of the phenomenon.Results: Four themes emerged from the grounded theory approach. The themes that emerged are: Attrition as an interplay of multiple factors; Ways of coping with attrition; Changes and modifications; and Finding new meanings as the core category. The emergent theory provided an insight into the process that the participants went through and the perceived contributory factors leading to the attrition.Conclusions: Understanding the interplay of these factors paved for a better understanding on how current and future students, faculty, and administrators can prevent attrition and assist returning students to become successful in the program towards completion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".