We used NVivo version 9 software (QSR International, London, UK) to abstract common themes represented in the text of the 109 VVDRS narratives that included at least 1 of our LTC search terms. We used qualitative text analysis (e.g., word frequency, text content coding)41 to identify salient features represented in these narratives, which we organized into 3 groups: (1) decedents residing in LTC at the time of their death, (2) decedents anticipating transitioning to LTC, and (3) decedents with a loved one (e.g., spouse, parent) living in or transitioning to LTC. We derived these groups empirically from the text content analysis, rather than hypothesizing them a priori.
We used NVivo version 9 software (QSR International, London, UK) to abstract common themes represented in the text of the 109 VVDRS narratives that included at least 1 of our LTC search terms. We used qualitative text analysis (e.g., word frequency, text content coding)41 to identify salient features represented in these narratives, which we organized into 3 groups: (1) decedents residing in LTC at the time of their death, (2) decedents anticipating transitioning to LTC, and (3) decedents with a loved one (e.g., spouse, parent) living in or transitioning to LTC. We derived these groups empirically from the text content analysis, rather than hypothesizing them a priori.
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We used NVivo version 9 software (QSR International, London, UK) to abstract common themes represented in the text of the 109 VVDRS narratives that included at least 1 of our LTC search terms. We used qualitative text analysis (eg, word frequency, text content coding) 41 to identify salient features represented in these narratives, which we organized into 3 groups: (1) decedents residing in LTC at the time of their death, (2) decedents. anticipating transitioning to LTC, and (3) decedents with a loved one (eg, spouse, parent) living in or transitioning to LTC. We derived these groups empirically from the text content analysis, rather than hypothesizing them a priori.
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