IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i9p5763-d811812.html
   My bibliography  Save this article

Correlation between Overconfidence and Learning Motivation in Postgraduate Infection Prevention and Control Training

Author

Listed:
  • Milena Trifunovic-Koenig

    (Institute for Infection Control and Infection Prevention, Health Care Association District of Constance (GLKN), 78315 Konstanz, Germany
    These authors contributed equally to this work.)

  • Stefan Bushuven

    (Institute for Infection Control and Infection Prevention, Health Care Association District of Constance (GLKN), 78315 Konstanz, Germany
    Hegau-Jugendwerk Hospital Gailingen, Health Care Association District of Constance (GLKN), 78262 Konstanz, Germany
    Institute of Medical Education, University Hospital, Ludwig Maximilian University of Munich, 80336 Munich, Germany
    These authors contributed equally to this work.)

  • Bianka Gerber

    (Institute of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Hegau-Bodensee-Hospital Singen, Health Care Association District of Constance (GLKN), 78224 Singen, Germany)

  • Baerbel Otto

    (Institute of Medical Education, University Hospital, Ludwig Maximilian University of Munich, 80336 Munich, Germany
    Institute of Laboratory Medicine, University Hospital, Ludwig Maximilian University of Munich, 80336 Munich, Germany)

  • Markus Dettenkofer

    (Institute for Infection Control and Infection Prevention, Health Care Association District of Constance (GLKN), 78315 Konstanz, Germany)

  • Florian Salm

    (Institute for Infection Control and Infection Prevention, Health Care Association District of Constance (GLKN), 78315 Konstanz, Germany)

  • Martin R. Fischer

    (Institute of Medical Education, University Hospital, Ludwig Maximilian University of Munich, 80336 Munich, Germany)

Abstract

Introduction: Training in hand hygiene for health care workers is essential to reduce hospital-acquired infections. Unfortunately, training in this competency may be perceived as tedious, time-consuming, and expendable. In preceding studies, our working group detected overconfidence effects in the self-assessment of hand hygiene competencies. Overconfidence is the belief of being better than others (overplacement) or being better than tests reveal (overestimation). The belief that members of their profession are better than other professionals is attributable to the clinical tribalism phenomenon. The study aimed to assess the correlation of overconfidence effects on hand hygiene and their association with four motivational dimensions (intrinsic, identified, external, and amotivation) to attend hand hygiene training. Methods: We conducted an open online convenience sampling survey with 103 health care professionals (physicians, nurses, and paramedics) in German, combining previously validated questionnaires for (a) overconfidence in hand hygiene and (b) learning motivation assessments. Statistics included parametric, nonparametric, and cluster analyses. Results: We detected a quadratic, u-shaped correlation between learning motivation and the assessments of one’s own and others’ competencies. The results of the quadratic regressions with overplacement and its quadratic term as predictors indicated that the model explained 7% of the variance of amotivation ( R 2 = 0.07; F (2, 100) = 3.94; p = 0.02). Similarly, the quadratic model of clinical tribalism for nurses in comparison to physicians and its quadratic term explained 18% of the variance of amotivation ( R 2 = 0.18; F (2, 48) = 5.30; p = 0.01). Cluster analysis revealed three distinct groups of participants: (1) “experts” ( n 1 = 43) with excellent knowledge and justifiable confidence in their proficiencies but still motivated for ongoing training, and (2) “recruitables” ( n 2 = 43) who are less competent with mild overconfidence and higher motivation to attend training, and (3) “unawares” ( n 3 = 17) being highly overconfident, incompetent (especially in assessing risks for incorrect and omitted hand hygiene), and lacking motivation for training. Discussion: We were able to show that a highly rated self-assessment, which was justified (confident) or unjustified (overconfident), does not necessarily correlate with a low motivation to learn. However, the expert’s learning motivation stayed high. Overconfident persons could be divided into two groups: motivated for training (recruitable) or not (unaware). These findings are consistent with prior studies on overconfidence in medical and non-medical contexts. Regarding the study’s limitations (sample size and convenience sampling), our findings indicate a need for further research in the closed populations of health care providers on training motivation in hand hygiene.

Suggested Citation

  • Milena Trifunovic-Koenig & Stefan Bushuven & Bianka Gerber & Baerbel Otto & Markus Dettenkofer & Florian Salm & Martin R. Fischer, 2022. "Correlation between Overconfidence and Learning Motivation in Postgraduate Infection Prevention and Control Training," IJERPH, MDPI, vol. 19(9), pages 1-20, May.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5763-:d:811812
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/9/5763/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/9/5763/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
    2. David L B Schwappach & Katrin Gehring, 2014. "Silence That Can Be Dangerous: A Vignette Study to Assess Healthcare Professionals’ Likelihood of Speaking up about Safety Concerns," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-8, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stefan Bushuven & Milena Trifunovic-Koenig & Michael Bentele & Stefanie Bentele & Reinhard Strametz & Victoria Klemm & Matthias Raspe, 2022. "Self-Assessment and Learning Motivation in the Second Victim Phenomenon," IJERPH, MDPI, vol. 19(23), pages 1-19, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carolina Laureti & Ariane Szafarz, 2016. "The price of deposit liquidity: banks versus microfinance institutions," Applied Economics Letters, Taylor & Francis Journals, vol. 23(17), pages 1244-1249, November.
    2. Elias Christopher J., 2015. "Percentile and Percentile-t Bootstrap Confidence Intervals: A Practical Comparison," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 153-161, January.
    3. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    4. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    5. Cyrus J. DiCiccio & Joseph P. Romano & Michael Wolf, 2016. "Improving weighted least squares inference," ECON - Working Papers 232, Department of Economics - University of Zurich, revised Nov 2017.
    6. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    7. Fallesen, Peter & Geerdsen, Lars Pico & Imai, Susumu & Tranæs, Torben, 2018. "The effect of active labor market policies on crime: Incapacitation and program effects," Labour Economics, Elsevier, vol. 52(C), pages 263-286.
    8. Badi H. Baltagi & Chihwa Kao & Long Liu, 2013. "The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 241-270, September.
    9. Torben Klarl, 2014. "Is Spatial Bootstrapping A Panacea For Valid Inference?," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 304-312, March.
    10. Olivier Armantier, 2006. "Estimates of Own Lethal Risks and Anchoring Effects," Journal of Risk and Uncertainty, Springer, vol. 32(1), pages 37-56, January.
    11. Lee, Taewook, 2016. "Wild bootstrap Ljung–Box test for cross correlations of multivariate time series," Economics Letters, Elsevier, vol. 147(C), pages 59-62.
    12. Oc, Burak & Daniels, Michael A. & Diefendorff, James M. & Bashshur, Michael R. & Greguras, Gary J., 2020. "Humility breeds authenticity: How authentic leader humility shapes follower vulnerability and felt authenticity," Organizational Behavior and Human Decision Processes, Elsevier, vol. 158(C), pages 112-125.
    13. Tedone, Archana Manapragada & Lanz, Julie J., 2024. "Staying silent during a crisis: How workplace factors influence safety decisions in U.S. nurses," Social Science & Medicine, Elsevier, vol. 341(C).
    14. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    15. Nicolas DEBARSY & Cem ERTUR, 2016. "Interaction matrix selection in spatial econometrics with an application to growth theory," LEO Working Papers / DR LEO 2172, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    16. Manolopoulos Dimitris & Salavou Helen & Papadopoulos Andrew & Xenakis Michail, 2024. "Strategic Decision-Making and Performance in Social Enterprises: Process Dimensions and the Influence of Entrepreneurs’ Proactive Personality," Entrepreneurship Research Journal, De Gruyter, vol. 14(2), pages 631-675, April.
    17. Emmanuel Flachaire, 2005. "More Efficient Tests Robust to Heteroskedasticity of Unknown Form," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 219-241.
    18. Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
    19. Jianghao Chu & Tae-Hwy Lee & Aman Ullah & Haifeng Xu, 2020. "Exact Distribution of the F-statistic under Heteroskedasticity of Unknown Form for Improved Inference," Working Papers 202027, University of California at Riverside, Department of Economics.
    20. Eric Blankmeyer, 2018. "Measurement Errors as Bad Leverage Points," Papers 1807.02814, arXiv.org, revised Mar 2020.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5763-:d:811812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.