Iranian Clinical Nurses’ Activities for Self-Directed Learning: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Clinical nurses need lifelong learning skills for responding to the rapid changes of clinical settings. One of the best strategies for lifelong learning is self-directed learning. The aim of this study was to explore Iranian clinical nurses' activities for self-directed learning. METHODS: In this qualitative study, 23 semi-structured personal interviews were conducted with nineteen clinical nurses working in all four hospitals affiliated to Isfahan Social Security Organization, Isfahan, Iran. Study data were analyzed by using the content analysis approach. The study was conducted from June 2013 to October 2014. FINDINGS: Study participants' activities for self-directed learning fell into two main categories of striving for knowledge acquisition and striving for skill development. The main theme of the study was 'Revising personal performance based on intellectual-experiential activities'. CONCLUSIONS: Study findings suggest that Iranian clinical nurses continually revise their personal performance by performing self-directed intellectual and experiential activities to acquire expertise. The process of acquiring expertise is a linear process which includes two key steps of knowledge acquisition and knowledge development. In order to acquire and advance their knowledge, nurses perform mental learning activities such as sensory perception, self-evaluation, and suspended judgment step-by-step. Moreover, they develop their skills through doing activities like apprenticeship, masterly performance, and self-regulation. The absolute prerequisite to expertise acquisition is that a nurse needs to follow these two steps in a sequential manner.
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 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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".