An Exploratory Study of Factors Influencing Resuscitation Skills Retention and Performance Among Health Providers
Bibliographic record
Abstract
INTRODUCTION: Resuscitation and life support skills training comprises a significant proportion of continuing education programming for health professionals. The purpose of this study was to explore the perceptions and attitudes of certified resuscitation providers toward the retention of resuscitation skills, regular skills updating, and methods for enhancing retention. METHODS: A mixed-methods, explanatory study design was undertaken utilizing focus groups and an online survey-questionnaire of rural and urban health care providers. RESULTS: Rural providers reported less experience with real codes and lower abilities across a variety of resuscitation areas. Mock codes, practice with an instructor and a team, self-practice with a mannequin, and e-learning were popular methods for skills updating. Aspects of team performance that were felt to influence resuscitation performance included: discrepancies in skill levels, lack of communication, and team leaders not up to date on their skills. Confidence in resuscitation abilities was greatest after one had recently practiced or participated in an update or an effective debriefing session. Lowest confidence was reported when team members did not work well together, there was no clear leader of the resuscitation code, or if team members did not communicate. DISCUSSION: The study findings highlight the importance of access to update methods for improving providers' confidence and abilities, and the need for emphasis on teamwork training in resuscitation. An eclectic approach combining methods may be the best strategy for addressing the needs of health professionals across various clinical departments and geographic locales.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".