研究背景：心理胰島素抗拒於糖尿病照護中是常見卻難以克服的問題，與病人的治療決策或用藥依從性密切相關，已有研究支持負面情緒會影響自我照護及血糖控制，然目前尚不清楚血糖控制、自我烙印及胰島素治療正面評價與心理胰島素抗拒間的相關性。
研究目的：探討在調整人口學與疾病變項下，糖尿病病人的血糖控制、自我烙印、胰島素治療正面評價與心理胰島素抗拒之相關。
研究方法：本研究採橫斷性、相關性研究設計，以方便取樣及結構式問卷方式於南部某醫學中心之內分泌新陳代謝科門診收案，資料收集時間為2025年04月07日至2025年07月03日，收案條件為需診斷為第一型或第二型糖尿病至少6個月、使用胰島素治療者、僅口服降血糖藥者於收案當日前三個月內的糖化血色素 > 7 %、年齡大於18歲、能以國台語溝通；排除條件為被醫師診斷或病人、家屬自訴具有精神疾病或認知障礙、血色素小於8 g/dL、影響血紅色素週期或糖化血色素檢測準確度的情況，如懷孕、慢性腎臟病第四、五期或使用類固醇等，共收案197位，研究工具包括人口學變項與疾病變項問卷、胰島素治療評價正面及負面分量表、自我烙印短版量表。統計方式使用描述性統計，包含次數分配、百分比、平均值、標準差、中位數及四分位距呈現資料分布狀況；使用推論性統計，包含獨立t檢定、卡方檢定、單因子變異數分析、皮爾森相關係數分析檢定變項間之相關性，以複迴歸分析階層法檢測心理胰島素抗拒之相關因子。
研究結果：本研究個案平均年齡58.82歲，55.8%為男性，86.3%為第二型糖尿病，平均罹患糖尿病時間15.15年，平均糖化血色素8.08%，自我烙印平均得分17.35分 (總分範圍9-36分)，胰島素治療正面評價平均分數14.97分(總分範圍4-20分)，心理胰島素抗拒平均分數43.88分 (總分範圍16-80分)。二變項分析結果顯示，與心理胰島素抗拒有關的變項有經濟狀況 (p = .04)、藥物治療方式 (p < .001)、是否使用胰島素 (p < .001)、胰島素注射者 (p < .001)、降血糖藥物類別數 (p = .005)、罹患糖尿病時間 (p = .03)、自我烙印 (p < .001) 和胰島素治療正面評價 (p < .001)。複迴歸分析結果顯示個案的教育程度越高(β = .19, p = .005)、自我烙印越嚴重 (β = .30, p <.001)、對胰島素治療正面評價較少(β = -.25, p <.001)，其心理胰島素抗拒也越高；而使用胰島素(β = -.39, p <.001)、使用五類降血糖藥物 (β = -.15, p = .04) 與使用一類降血糖藥物的個案相比，有較低的心理胰島素抗拒，上述複迴歸顯著的相關變項，可解釋心理胰島素抗拒之變異量43.6%。
研究結論：建議未來醫護人員可於個案診斷糖尿病始、使用三種以上降血糖藥物類別、開始使用胰島素治療或用藥依從性不佳時，使用評估工具評估個案自我烙印的變化，運用溝通技巧深入了解其感受，必要時轉介心理諮商輔導。為強化個案對胰島素治療的正面態度，政府或糖尿病相關團體舉辦講座時，可納入研究實證結果佐證胰島素效益，依個案教育程度建立初階或進階衛教教材，可透過公眾人物影響力製作影音或圖像文宣，增強個案的正確用藥認知，逐漸建立對胰島素治療的正面回饋，同時降低個案的心理胰島素抗拒。

The purpose of this study was to investigate the relationship between glycemic control, self-stigma, positive insulin treatment appraisals, and psychological insulin resistance (PIR) in people with diabetes mellitus. A cross-sectional correlational research design, structured questionnaires, and convenience sampling techniques were used. Data were collected from April 2025 to July 2025 at the endocrinology and metabolism outpatient clinic of a medical center in southern Taiwan. A total of 197 participants were enrolled. Instruments included demographic and disease variables, positive and negative appraisals of insulin treatment, and the short form self-stigma scale. The statistical methods included descriptive statistics such as frequency distributions, percentages, means, standard deviations, medians, and interquartile ranges, as well as inferential statistics, including independent t-tests, chi-square tests, one-way analysis of variance, and Pearson correlation coefficient analysis to examine variable correlations. A multiple hierarchical regression analysis was conducted to identify important factors associated with PIR. This study showed that higher educational level, greater self-stigma, and fewer positive appraisals of insulin treatment were associated with a higher PIR, and current insulin use and treatment with five (vs. one) glucose-lowering medication classes were associated with a lower PIR.

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