The Effects of Clinical Placement on Students' Confidence in Their Mental Health Nursing Competencies
Bibliographic record
Abstract
Concerns have been expressed about the ability of nov- ice nurses to provide competent mental health care (Tognazinni, Davis, Kean, Osborne, & Wong, 2008; Wyn- aden, Orb, McGowan, & Downie, 2000). This study eval- uated and compared the effects of a clinical placement on the confidence of baccalaureate nursing students in their mental health competencies. The study explored two re- search questions: 1) Does completing a clinical mental health course affect students' confidence in their mental health nursing competencies? 2) Do the confidence levels of students whose clinical courses take place in commu- nity settings differ from those whose clinical courses take place in acute in-patient units?BackgroundThe existing literature shows that certain variables may positively influence undergraduate baccalaureate nursing students' acquisition of mental health nursing competen- cies; specifically, the inclusion of a mental health theory course (Bairan & Farnsworth, 1989; Happell, 2009) and a clinical mental health course (Happell, 2008a, 2008b; Happell, Robins & Gough, 2008a, 2008b). A number of researchers have found that the clinical mental health course was optimally situated in a community setting (Happell, 2008c; McAllister, 2007; Wynaden et al., 2000), whereas one source found that hospital in-patient settings yielded better learning outcomes (Henderson, Happell, & Martin, 2007).MethodsampLe anD instRumentA voluntary convenience sample was obtained that con- sisted of undergraduate BSN students, with 20 situated in the in-patient hospital unit and 14 in community clinical settings. Pre- and posttest questionnaires were adminis- tered. The Mental Health Nursing Clinical Confidence Scale (Bell, Horsfall, & Goodin, 1998), a recognized instrument with well-established validity, was utilized. This instrument consists of a series of self-report questions, rated on a seven-point Likert scale, that evaluate the stu- dents' confidence in specific mental health care nursing competencies. Consent was obtained from all participants, and the project obtained formal approval from the partici- pating institution's research ethics board.Data AnalysisThe data were analyzed using SPSS (Version 14.0), with a generalized linear model (GLM) repeated measures analy- sis to control for type 1 error. The confidence scores were totaled to obtain each participant's total score for the pre- test and for the posttest. These values were used to analyze the data. The statistical analysis was conducted at a 95 percent confidence level with a p-value ResultsA Mauchly's test of sphericity was W = 1, which was great- er than a normal distribution of 0.05, so sphericity was as- sumed. The repeated measures analysis of the within-sub- jects variable showed a significant difference for tests (F = 18.599, p The results of this study provided support to two con- clusions: 1) Students' confidence in their mental health nursing competencies improved following their clini- cal mental health placement, supporting the findings of previous studies and underscoring the importance of including a clinical mental health course in undergrad- uate nursing programs. 2) The specific type of clinical placement (community versus in-patient) was not shown to have a significant effect on confidence levels, contra- dicting previous data.Discussion/Recommendations for ResearchUnforeseen implications arose that were not addressed in the existing literature. …
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".