堀口 涼子

目的 日本において,マタニティハラスメント(以下,マタハラ)と職場環境要因について調べた研究はほとんどない。本研究では,妊娠中の従業員を対象にインターネットによる横断調査を実施し,マタハラと関連する職場環境要因について,探索的に検討することを目的とした。方法 COVID-19による日本における最初の緊急事態宣言中であった2020年5月22日～31日の期間に,就労妊婦(妊娠判明時に就労していた女性を含む)379名を対象とし,インターネットによる横断調査を実施した。マタハラは,国のガイドラインで禁止されている16の不利益取扱いを1つ以上受けているかどうかで定義した。分析は,ロジスティック回帰分析を用いた。結果 対象者379名中,マタハラの経験がある者は97名(25.6%)であった。年齢や妊娠週数を調整した全変数調整モデルにおいて,企業規模が1,000人以上と比較して,1-29人(オッズ比(OR):3.55,95%信頼区間(95%CI):1.49-8.45),30-299人(OR:2.66,95%CI:1.29-5.51)の環境でマタハラの経験が多かった。一方,上司からのサポート(OR:0.80,95%CI:0.69-0.92)の得点が高いほど,マタハラの経験は少なくなる傾向がみられた。全変数調整モデルでは有意ではなかったが,無調整モデルにおいて,雇用形態では正社員と比べてパート社員で,職種では管理職・専門・技術職と比べて営業・販売職・サービス業で,マタハラに対応する相談窓口の設置に関しては「ある」と比べて「ない」環境で,有意にマタハラの経験が多かった。一方,勤続年数が長いほど,同僚のサポートの得点が高いほど,マタハラの経験は少なくなる傾向がみられた。結論 職場環境要因として企業規模が小さいことはマタハラの経験が多いことと関連していた。一方,上司からのサポート得点が高いことは,マタハラの経験が少ないことと関連していた。また,パート社員,営業・販売職・サービス業,マタハラ対応相談窓口の設置がないこともマタハラの経験が多い傾向がみられた。同僚からのサポート得点が高い,または勤続年数が長いとマタハラの経験が少ない傾向がみられた。特に規模の小さい企業側は,マタハラを予防するために,法律に沿ったマタハラ防止対策を講じるとともに,上司や同僚からのサポートの高い職場環境を作る必要がある。(著者抄録)

Objectives The objective of this study was to examine the structural validity and internal consistency of the original English version of the Patient Centred Assessment Method (PCAM) in a primary care setting in a Japanese island area. Design Cross-sectional study. Setting A clinic on a remote island in Okinawa, Japan, that provides general outpatient and 24-hour emergency services. Participants This study included 355 patients who visited Tarama Clinic from 1 April 2018 to 30 June 2018, were aged ≥20 years, lived in Tarama Village and had decision-making capacity. Main outcome measures Patient complexity scored by the PCAM. Results The mean (SD) PCAM score was 21.4 (5.7). The distribution was skewed to the right and there were no ceiling and floor effects. Confirmatory factor analysis found that the previously reported two-factor and three-factor structures did not show a good fit (root mean square error of approximation 0.18 and 0.16, comparative fit index 0.83 and 0.89 and standardised root mean square residual 0.14 and 0.11, respectively). Exploratory factor analysis revealed a new two-factor structure: ‘Biomedical complexity’ and ‘Psychosocial complexity’. The Cronbach’s alpha values for the total PCAM score, the ‘Biomedical complexity’ factor, and the ‘Psychosocial complexity’ factor were 0.81, 0.82 and 0.74, respectively. Conclusions In this study, confirmatory factor analysis found that the data did not fit sufficiently using the previously reported two-factor and three-factor structures. Instead, exploratory factor analysis revealed a new two-factor structure, for which the Cronbach’s alpha values exceeded the threshold level. Therefore, the structural validity and internal consistency of the English version of the PCAM were verified in a primary care setting in a Japanese island area.

Attitudes Toward Health Care Teams Scale(ATHCTS)の医師中心性のサブスケール日本語版として、Adapted Physician Centrality Scale(APCS)を開発し、その内部整合性と構造妥当性を検証した。自己記入質問票を用いて横断的研究を行い、内部整合性と因子構造の統計的適合性を評価した。487名のフルタイム看護師のうち307名(女性288名、男性17名、性別不明2名、平均31.5±9.68歳)が研究に参加した。2項目を除いた後、APCSはATHCTSの医師中心性に類似した内部整合性(Cronbachのα係数;0.63)を示した。確認的因子分析では許容できるレベルの統計的適合性(standard root mean square residual;0.09以下、比較適合指数;0.96以上)を示した。APCSは日本の医師中心性を評価するために許容できる内部整合性と構造的妥当性を持つことが示された。

Objectives The primary objective of this study was to develop the Japanese version of the Patient Centred Assessment Method (PCAM) and its user guide. The secondary objective was to examine the validity and reliability in the primary care setting. Design Cross-sectional study. Setting Three family physician teaching clinics located in urban residential areas in Tokyo, Japan. Participants Patients who were aged 20 years or older, and who had an appointment with physicians at the three participating clinics. Main outcome measures Patient complexity measured by PCAM and complexity/burden level measured by a Visual Analogue Scale (VAS). Results Although confirmatory factor analysis using a model described in a previous study revealed that the indices did not meet the criteria for good fit, exploratory factor analysis revealed a new three-factor structure of ‘Personal well-being,’ ‘Social interaction’ and ‘Needs for care/service.’ Cronbach’s alpha of PCAM was 0.86. Spearman’s rank correlation coefficients between PCAM scores and VAS scores were 0.51 for complexity (p<0.001) and 0.41 for burden (p<0.001). There were 42 patients (14.3% of total patients) with PCAM scores greater than its mean of 16.5 but with complexity VAS scores less than its mean of 20.8. Conclusions The Japanese version of PCAM and its user guide were developed through Japanese translation and cultural adaptation by cognitive debriefing. PCAM is a valid and reliable tool to assess patient complexity in the primary care settings in Japan. Additionally, although the correlation between total PCAM scores and complexity/burden as assessed by VAS was moderate, PCAM can more precisely identify patient complexity than skilled physician’s intuition.

Objectives An ageing society includes high patient complexity. Various biopsychosocial problems result in a high burden for health-related professionals. The direct relationship between the burden and patient complexity, however, has not been reported. We aimed to examine correlations between the burden for the attending physicians and nurses, and Patient Centred Assessment Method (PCAM) scores of patient complexity. Design Prospective cohort study. Setting A regional secondary care hospital in Japan. Participants We included all inpatients admitted to our acute care unit between 1 July 2014 and 30 September 2014. Exclusion criteria were age <20 years, refusal to participate in the study and length of stay fixed at the time of admission. Main predictor PCAM total score in the initial phase of hospital admission. Main outcome The burden for each profession (measured on a Visual Analogue Scale). Results In total, 201 inpatients participated [female/male=98/103, mean (SD) age of 77.4±11.9 years]. Spearman’s rank correlation coefficients between the burden and the PCAM score ranged from 0.23 to 0.32. All p values were <0.05. Multivariate analysis was conducted using multilevel mixed-effects linear regression to determine the association between the burden and the PCAM score in two models. Model 1 used the total PCAM score as the predictive variable. Model 2 used the PCAM factors, patient-oriented complexity and medicine-oriented complexity, as predictive variables. In Model 2, with the burden of physicians, medicine-oriented complexity was statistically significant, whereas with the burden of nurses, both age and patient-oriented complexity were statistically significant. Conclusions PCAM scores correlated with the burden for physicians and nurses. Individual PCAM factors affected the burden for each profession differently.