We will consider two examples specific to Lebanon whose effects lead to population-level response shifts that modified Health Related Quality of Life (HRQL): the 2006 war in Lebanon and the Lebanese smoking ban. These examples serve two purposes. First, they assert that population shifts are possible and have happened before. Thus, an initiative to produce a positive response shift may be effective. Second, they illustrate that positive population level shifts might not be apparent at first glance. Nevertheless, a deeper analysis could reveal immense progress on the wellbeing for when an initiative to produce a positive response shift appears ineffective, one would first need to revise the metric being evaluated before jumping to conclusions.

The 2006 Lebanon war provided an example of a traumatic event affecting a large population. Elders in Lebanon are a vulnerable group during a war, regardless of what part of the country they reside in. During the 2006 war, a majority of Lebanon’s residents reported increased symptoms of Post Traumatic Stress Disorder (PTSD) [5] , depression, frailty, malnutrition and lack of self care [6] . However, positive effects have also been observed including resilience, closer relationships with others, tighter bonds with neighbors, increased compassion and spirituality, empathy and help among refugees or internally displaced people from the eldest to the youngest [7] . Those elders who experienced hope and spirituality experienced lower anxiety and depression, greater satisfaction with life and better health [7] [8] . Response shift in this example was considered at a population-level as everyone shifted at the same time leading to a social trend, after a national catastrophe and in the health status of the whole population.

The smoking ban in Lebanon provided another example of population-level response shift. Smoking ban is likely to induce a population-level shift including wellbeing, prosperity, diet and social norms though affecting the prevalence of smoking as a social pressure. A substantial proportion of smokers (81%) started perceiving little social approval for their smoking (this shift in perception referred to as denormalization), would have fewer places to smoke [9] and regretted smoking ever [10] . This denormalization triggered a reconceptualization of HRQL to include the negative effects of smoking.

Taken the two examples above to assess the impact of a training initiative on response shift could be based on pre- and post-workshop and training session tests [11] . Such an approach would hinge on quantifying only the knowledge acquired by the participants during the training sessions, and might fail to represent their knowledge/experience in elderly care. One would seek to understand whether or not the assessment captured the features of a potential response shift: the goal of the initiative would be not to increase knowledge, but to foster a general understanding and encourage a practical approach to elderly care. In this respect, the results of a pre- and post-test could only be relevant in determining how effective the training sessions were in conveying information to the participants. One would not jump to conclusions regarding the population level shift, regardless of whether or not the test showed that the training had a positive impact on trainees.

Furthermore, the dataset provided by the pre- and post-test presented a cross sectional assessment. A long-term analysis would necessarily require a sequence of follow-up evaluations timed accordingly. Those should not only focus on detecting an increase of knowledge and confidence, but would also include long term evaluation questions assessing the impact of the training in implementing policy, clinical, education and/or research activities in the communities.

We expect that response-shifts at the level of the Lebanese population would be better achieved if the cared-for cases were “organic” (i.e. “home-grown”). For example, and drawing on our own experience in the field, it would be beneficial to allow the social workers under training to get to know the elders they care for better, and incentivize them to work specifically in the geriatric field [3] . Population-shift in geriatric care in Lebanon would more likely occur and positively alter the HRQL capitalizing we capitalize on human bond, compassion and support to build networks and community programs to serve elders. Since family and person-to-person bonds are the country’s forte [12] , capitalizing on family dynamics in Lebanon would likely increase HRQL.

Also, while planning the trainings, many factors ought to be taken into consideration. Beyond general logistics (e.g. weather, number of participants, holiday schedule…) trainers would need to account for the political and cultural contexts in some societies [13] . This issue has special relevance for older adults in Lebanon as they endured a 16 year civil war and weathered many sectarian conflicts. Identity matching based on religious or political affiliation might offer an opportunity for collecting data when conducting surveys or focus groups. Such a matching would encourage the participants to open up, create a trusting atmosphere and help establish a bond based on a perceived shared identity.

The legal and other implications of aging are many and complex, and implementing resources to address potential concerns need to be a part of programs targeting geriatric care. Given this, the authors would like to caution that specific interpretations of population-level response shift would add complexity to the program selection. Nevertheless, geriatric HRQL in the Lebanese population would not occur without taking these factors into consideration. These factors are mandatory in the selection and interpretation of preferential functions for HQOL economic applications such as pushing a bill covering elders’ medical services and allowing for continuous medical insurances and elder pensions and benefits.