Principaux résultats sylvicoles obtenus en 1998

Premises for a social network approach to self-management focussed on engagement and preference. (DOCX 38.2 kb

Additional file 2: of Implementing a social network intervention designed to enhance and diversify support for people with long-term conditions. A qualitative study

Training outline. (DOCX 16 kb

Additional file 3: of Implementing a social network intervention designed to enhance and diversify support for people with long-term conditions. A qualitative study

Use of normalisation process theory as framework for analysis of video in case study 2. (DOCX 23.3 kb

Modelled relationships (mean score and 95% confidence interval) between health behaviour and illness work done by the network, and emotional well-being and illness work done by the network, controlled for patient sociodemographics, disease burden and other significant social network characteristics (Model 3).

<p>Modelled relationships (mean score and 95% confidence interval) between health behaviour and illness work done by the network, and emotional well-being and illness work done by the network, controlled for patient sociodemographics, disease burden and other significant social network characteristics (Model 3).</p

Summary of regression analyses of changes in outcomes: Self-management outcomes.

<p>*p< = 0.05;</p><p>**p<0.01;</p><p>***p< = 0.001.</p>$<p>Statistically significant (p<0.05) under sensitivity analysis.</p>1<p>Variables with no significant relationships to any outcome are not shown.</p><p>Model 1 = individual network characteristics controlled for patient demographic variables.</p><p>Model 2 = individual network characteristics controlled for patient demographics and disease burden.</p><p>Model 3 = network characteristics controlled for demographics, disease burden and one-another.</p

Summary of regression analyses of outcomes at Time 1: Health status outcomes.

<p>*p< = 0.05;</p><p>**p<0.01;</p><p>***p< = 0.001.</p>1<p>Variables with no significant relationships to any outcome are not shown.</p><p>Model 1 = individual network characteristics controlled for patient demographic variables.</p><p>Model 2 = individual network characteristics controlled for patient demographics and disease burden.</p><p>Model 3 = network characteristics controlled for demographics, disease burden and one-another.</p

Summary of regression analyses of outcomes at Time 1: Self-management outcomes.

<p>*p< = 0.05;</p><p>**p<0.01;</p><p>***p< = 0.001.</p>1<p>Variables with no significant relationships to any outcome are not shown.</p><p>Model 1 = individual network characteristics controlled for patient demographic variables.</p><p>Model 2 = individual network characteristics controlled for patient demographics and disease burden.</p><p>Model 3 = network characteristics controlled for demographics, disease burden and one-another.</p

Summary of regression analyses of changes in outcomes: Health status outcomes.

<p>*p< = 0.05;</p><p>**p<0.01;</p><p>***p< = 0.001.</p>$<p>Statistically significant (p<0.05) under sensitivity analysis.</p>1<p>Variables with no significant relationships to any outcome are not shown.</p><p>Model 1 = individual network characteristics controlled for patient demographic variables.</p><p>Model 2 = individual network characteristics controlled for patient demographics and disease burden.</p><p>Model 3 = network characteristics controlled for demographics, disease burden and one-another.</p

Descriptive statistics of the sample.

1<p>Used as a continuous variable in regression analysis.</p

Network type by delineating characteristics^*.

<p>Network type by delineating characteristics^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161027#t001fn001" target="_blank">*</a>}.</p