İTAKİ Düşme Riski Ölçeği'nin Psikometrik Özelliklerinin Değerlendirilmesi

Yıl 2020, Cilt: 13 Sayı: 4, 214 - 221, 15.10.2020

Veysel Karani Barış , Şeyda Seren İntepeler , Serap İleri , Hacer Rastgel

https://doi.org/10.46483/deuhfed.732097

Cited By: 12

Öz

Giriş: Hasta düşmelerinin önlenebilmesi için ilk olarak hastaların düşme risk faktörlerinin ve bu risk faktörlerine yönelik önleyici girişimlerin belirlenmesi gerekmektedir. Hastaların düşme risk faktörlerinin doğru belirlenebilmesi içinse geçerli ve güvenilir risk değerlendirme aracına ihtiyaç vardır. Amaç: Araştırmanın amacı, İTAKİ Düşme Riski Ölçeği’nin psikometrik özelliklerinin değerlendirilmesidir. Yöntem: Vaka-kontrol metodolojik tasarıma sahip olan araştırma, bir üniversite ve eğitim araştırma hastanesinde gerçekleştirilmiştir. Vaka ve kontrol grubunda yer alan toplam 605 hastanın bilgilerine hastanelerin bilgi işlem birimlerinden ve hasta dosyalarından ulaşılmıştır. İTAKİ Düşme Riski Ölçeği’nin güvenirliği Cronbach alpha katsayısı ve madde toplam puan korelasyonu, geçerliği ise alıcı işlem karakteristiği (ROC eğrisi), duyarlılık, özgüllük, pozitif ön görü değeri ve negatif öngörü değerleri hesaplanarak değerlendirilmiştir. Bulgular: İTAKİ Düşme Riski Ölçeği’nin Cronbach alpha katsayısı .46 olarak hesaplanmıştır. Gerçekleştirilen madde toplam puan korelasyon analizi sonucunda ölçekte yer alan altı maddenin toplam puan ile istatistiksel olarak anlamlı bir korelasyona sahip olmadığı saptanmıştır. İTAKİ Düşme Riski Ölçeği’nin duyarlılık değeri .91, özgüllük değeri .17, pozitif öngörü değeri .36, negatif öngörü değeri ise .78 olarak hesaplanmıştır. ROC analizi sonucunda ROC Eğrisi Altında Kalan Alan .58 olarak hesaplanmıştır (p = .006, %95 Güven Aralığı = .53 - .64). Sonuç: Araştırma kapsamında İTAKİ Düşme Riski Ölçeği’nin güvenirliği ve ayırım gücü düşük saptanmıştır. Sonraki araştırmalarda ölçeğin revize edilerek daha geniş bir örneklemde uygulanması önerilir.

Anahtar Kelimeler

Hasta Düşmesi, Düşme Risk Faktörü, İTAKİ, Psikometrik Özellikler

Destekleyen Kurum

Destekleyen herhangi bir kurum bulunmamaktadır.

Evaluation of Psychometric Properties of ITAKI Fall Risk Scale

Öz

Background: In order to prevent patient falls, it is first necessary to determine the fall risk factors and preventive interventions for these factors. To determine the fall risk factors of patients correctly, a valid and reliable risk assessment tool is needed. Objectives: The aim of the research is to evaluate the psychometric properties of the ITAKI Fall Risk Scale. Methods: The research is case-control methodologically designed and conducted at a university and educational research hospital. The information of 605 patients in the case and control groups was accessed from the computing software of the hospitals and patient files. The reliability of the scale was evaluated by calculating the Cronbach alpha coefficient and item total score correlation, and validity was evaluated by ROC curve, sensitivity, specificity, positive predictive value and negative foresight values. Results: Cronbach alpha coefficient of the scale was calculated as .46. It was determined that the six items in the scale did not have a statistically significant correlation with the total score. The sensitivity value of the scale was calculated as .91, specificity value .17, positive prediction value .36 and negative prediction value .78. As a result of the ROC analysis, the Under the Area of ROC Curve was calculated as .58 (p = .006, 95% Confidence Intervals = .53 - .64). Conclusion: Reliability and discrimination validity of ITAKI Fall Risk Scale were found low. For future research, it is recommended that the scale is revised and applied in a larger sample.

Anahtar Kelimeler

Patient Falls, Fall Risk Factor, ITAKI, Psychometric Properties

Kaynakça

• 1. D’Amour D, Dubois CA, Tchouaket E, Clarke S, Blais R. The occurrence of adverse events potentially attributable to nursing care in medical units: cross sectional record review. Int J Nurs Stud. 2014;51(6):882–891.
• 2. Schoene D, Heller C, Aung YN, Sieber CC, Kemmler W, Freiberger E. A systematic review on the influence of fear of falling on quality of life in older people: is there a role for falls?, Clin Interv Aging, 2019;14:701–719.
• 3. Baris VK, Seren Intepeler S, Yeginboy EY. The cost of serious patient fall‐related injuries at hospitals in Turkey: A matched casecontrol study. Clin Nurs Res. 2018;27(2):162–179.
• 4. Wong CA, Recktenwald AJ, Jones ML, Waterman BM, Bollini ML, Dunagan WC. The cost of serious fall‐related injuries at three Midwestern hospitals. Jt Comm J Qual Patient Saf. 2011;37:81–87.
• 5. Zecevic AA, Chesworth BM, Zaric GS, Huang Q, Salmon A, McAuslan D et. al. Estimating the cost of serious injurious falls in a Canadian acute care hospital. Can J Aging 2012;31:139–147.
• 6. Dunne TJ, Gaboury I, Ashe MC. Falls in hospital increase length of stay regardless of degree of harm. J Eval Clin Pract. 2014;20:396–400.
• 7. Baris VK, Seren Intepeler S. Views of key stakeholders on the causes of patient falls and prevention interventions: A qualitative study using the international classification of functioning, disability and health, J Clin Nurs. 2019;28:615–628.
• 8. Deandrea S, Bravi F, Turati F, Lucenteforte E, Vecchia CL, Negri E. Risk factors for falls in older people in nursing homes and hospitals. A systematic review and meta-analysis, Arch Gerontol Geriatr. 2013;56:407–415.
• 9. O'Neil CA, Krauss J, Bettale J, PharmD K, Costantinou E, Dunagan C et. al. Medications and patient characteristics associated with falling in the hospital, J Patient Saf. 2018;14(1):27–33.
• 10. Moe K, Brockopp D, McCowan D, Merritt S, Hall B. Major predictors of inpatient falls a multisite study, JONA 2015;45(10):498-502.
• 11. Poe SS, Dawson PB, Cvach M, Burnett M, Kumble S, Lewis M et. al. The Johns Hopkins Fall Risk Assessment Tool a study of reliability and validity, J Nurs Care Qual. 2018;33(1):10–19.
• 12. Kim EA, Mordiffi SZ, Bee WH, Devi K, Evans D. Evaluation of three fall-risk assessment tools in an acute care setting. J Adv Nurs 2007;60(4):427‐435.
• 13. Degelau J, Belz M, Bungum L, Flavin PL, Harper C, Leys K, Lundquist L, Webb B. Prevention of falls (Acute Care). Institute for Clinical Systems Improvement. April 2012.
• 14. Registered Nurses’ Association of Ontario -RNAO- Preventing falls and reducing ınjury from falls. 4th ed. Registered Nurses’ Association of Ontario; 2017.
• 15. Cameron ID, Gillespie LD, Robertson MC, Murray GR, Hill KD, Cumming RG et al. Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database Syst. Rev. 2012;12, CD005465.
• 16. Chari S, McRae P, Varghese P, Ferrar K, Haines TP. Predictors of fracture from falls reported in hospital and residential care facilities: a cross-sectional study. BMJ Open 2013;3(8):e002948.
• 17. Petitpierre NJ, Trombetti A, Carroll I, Michel JP, Herrmann FR. The FIM instrument to identify patients at risk of falling in geriatric wards: a 10-year retrospective study. Age Ageing 2010;39(3):326–331.
• 18. Ivziku D, Matarese M, Pedone C. Predictive validity of the Hendrich fall risk model II in an acute geriatric unit. Int J Nurs Stud. 2011;48(4):468–474.
• 19. Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fall-prone patient. Can J Aging 1989;8:366–371.
• 20. Hendrich A, Nyhuis A, Kippenbrock T, Soja ME. Hospital falls: developing of a predictive model for clinical practice. Appl Nurs Res 1995;8:129–139.
• 21. Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ. 1997;315(7115):1049‐1053.
• 22. Chapman J, Bachand D, Hyrkäs K. Testing the sensitivity, specificity and feasibility of four falls risk assessment tools in a clinical setting. J Nurs Manag. 2011;19(1):133‐142.
• 23. Park SH. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res. 2018;30(1):1‐16.
• 24. Sağlık Bakanlığı, Sağlıkta Kalite Standartları - Hastane (Versiyon 5, Revizyon-01), Sağlık Hizmetleri Genel Müdürlüğü Sağlıkta Kalite ve Akreditasyon Daire Başkanlığı, 2016, Ankara.
• 25. Tabachnick BG, Fidell LS. Using multivariate statistics. 6th ed. Pearson; 2013.
• 26. Flahault A, Cadilhac M, Thomas G. Sample size calculation should be performed for design accuracy in diagnostic test studies. J Clin Epidemiol. 2005;58(8):859‐862.
• 27. Jung H, Park HA. Testing the Predictive Validity of the Hendrich II Fall Risk Model. West J Nurs Res. 2018;40(12):1785‐1799.
• 28. Akgül A. Tıbbi Araştırmalarda İstatistiksel Analiz Teknikleri ‘SPSS Uygulamaları’. 3. Basım, Emek Ofset; 2005; 180-396.
• 29. Chow SK, Lai CK, Wong TK, Suen LK, Kong SK, Chan CK et al. (Evaluation of the Morse Fall Scale: applicability in Chinese hospital populations. Int J Nurs Stud. 2007;44(4):556‐565.
• 30. Yılmaz Demir N, Seren İntepeler Ş. Morse düşme ölçeğinin Türkçe’ye uyarlanması ve duyarlılık-seçicilik düzeyinin belirlenmesi, Ege Üniversitesi Hemşirelik Fakültesi Dergisi 2012;28(1):57-71.
• 31. Zhang C, Wu X, Lin S, Jia Z, Cao J. Evaluation of Reliability and Validity of the Hendrich II Fall Risk Model in a Chinese Hospital Population. PLoS One. 2015;10(11):e0142395.
• 32. Gözüm S, Aksayan S. Kültürlerarası ölçek uyarlaması için rehber II: Psikometrik özellikler ve kültürlerarası karsılastırma. Hemsirelikte Arastırma Gelistirme Dergisi 2003;5(1):3-14.
• 33. Nunnally C, Bernstein H, Psychometric Theory. 3th ed. McGraw-Hill; 1994.
• 34. Hayakawa T, Hashimoto S, Kanda H, Hirano N, Kurihara Y, Kawashima T et al. Risk factors of falls in inpatients and their practical use in identifying high-risk persons at admission: Fukushima Medical University Hospital cohort study. BMJ Open. 2014;4(8):e005385.
• 35. Akobeng AK. Understanding diagnostic tests 1: sensitivity, specificity and predictive values, Acta Pædiatrica. 2007;96:338–341.
• 36. Baek S, Piao J, Jin Y, Lee SM. Validity of the Morse Fall Scale implemented in an electronic medical record system. J Clin Nurs. 2014;23(17-18):2434‐2440.
• 37. Watson BJ, Salmoni AW, Zecevic AA. The use of the Morse Fall Scale in an acute care hospital, Clin Nurs Stud. 2016;4(2):32-40.
• 38. Klinkenberg WD, Potter P. Validity of the Johns Hopkins Fall Risk Assessment Tool for Predicting Falls on Inpatient Medicine Services. J Nurs Care Qual. 2017;32(2):108‐113.
• 39. Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med. 2013;4(2):627‐635.
• 40. Soydemir D, Seren Intepeler S, Mert H. Barriers to medical error reporting for physicians and nurses. West J Nurs Res. 2017;39(10):1348‐1363.
• 41. Hill A, Hoffmann T, Hill KD, Oliver D, Beer C, Mcphail S et al. Measuring falls events in acute hospitals-a comparison of three reporting methods to identify missing data in the hospital reporting system. J Am Geriatr Soc. 2010;58(7):1347‐1352.
• 42. Shorr RI, Mion LC, Chandler AM, Rosenblatt LC, Lynch D, Kessler LA. Improving the capture of fall events in hospitals: combining a service for evaluating inpatient falls with an incident report system. J Am Geriatr Soc. 2008;56(4):701‐704.