How to Measure Reach in Social Prescribing, Not Just Outcomes

A social prescribing link worker sees 40 people this quarter. Thirty-one report feeling less lonely three months on. That is a good outcome result — and it tells you almost nothing about whether the service is working, because it says nothing about the other 400 people on the practice’s list who were never referred, never told about it, or referred and never turned up.

This is the gap most social prescribing evaluations leave open. They measure what happened to the people who came through the door. They rarely measure the door itself: who could reach it, who was offered it, and who dropped off before the first session. Funders ask for outcomes because outcomes are what justify continued funding. But an outcomes report built on a self-selected group of engaged, motivated referrals will almost always look good, regardless of whether the service is reaching the people it was designed for.

Why reach and outcomes answer different questions

Outcome measures answer: did this help the people we saw? Reach measures answer: who did we see, and who did we miss? A service can score well on the first and badly on the second — and often does, because people who complete an intervention and respond to a follow-up survey are, by definition, the ones for whom the service was easiest to deliver.

The systematic review of social prescribing and wellbeing published in 2021 found consistent improvements in self-esteem and self-confidence among people who engaged with services, but noted the underlying trial evidence is thin and heterogeneous across programmes. A separate 2021 systematic review focused specifically on loneliness found that all nine included studies reported positive impacts for the individuals studied. Read that sentence again: for the individuals studied. Neither review can tell you what share of the eligible population those individuals represent, because that is not what the underlying studies were designed to capture.

The reach problem matters more in social prescribing than in most interventions because the people most likely to benefit from social connection are frequently the people least likely to self-refer. AARP’s 2018 survey of adults 45 and older found the strongest predictors of loneliness were the size and diversity of a person’s social network and physical isolation — the very factors that make it harder to hear about a service, get to it, or trust that it applies to you. The same survey found 61% of people who never speak to their neighbours report loneliness, against 33% of those who do. If a social prescribing pathway depends on a GP noticing and referring, or on a patient self-advocating during a ten-minute appointment, it will systematically underreach the isolated.

What this means in practice: report reach and outcomes side by side, every time, to every funder. A programme that improves outcomes for 90% of a narrow, easy-to-reach slice of the eligible population is a different achievement — and a different funding case — than one that improves outcomes for 60% of a broad, representative slice. Funders should be told which one they are looking at.

The five numbers to track

You do not need a research department to measure reach. You need five numbers, tracked at each stage of the pathway, ideally against the demographic profile of the population you’re meant to be serving.

1. Eligible population. Everyone who fits the referral criteria — not everyone referred. This usually comes from practice or service registers, not from your own caseload data.
2. Aware population. Everyone who was told the service exists, through a poster, a GP conversation, a leaflet, a community outreach event. This is almost never tracked, and it is where the biggest drop-off usually happens.
3. Referred population. Everyone formally referred in, whatever the route.
4. Engaged population. Everyone who attends at least one session or contact.
5. Completed population. Everyone who finishes the intended course of contact, however that is defined for your service.

Report these as a funnel, not as a single conversion rate. A funnel shows you where people are lost. A single “80% satisfaction among completers” number hides everything upstream of completion.

A five-step process for building this

Step 1: Define the eligible population before you launch, not after. Use practice registers, ward-level demographic data, or referral criteria to establish who the service is meant to reach. Without this baseline, every later number is uninterpretable.

Step 2: Set demographic categories that match known risk factors, not just convenient ones. Age, living alone status, and self-reported network size predict loneliness better than postcode alone. The National Academies’ 2020 report on older adults notes that roughly a quarter of adults 65 and older are socially isolated, and recommends the health care system routinely assess isolation rather than wait for it to surface incidentally — the same logic applies to tracking who your service reaches, not just who it helps.

Step 3: Instrument the awareness stage. This is the step almost everyone skips, because it is genuinely hard to measure who heard about a service and decided not to act on it. Even a rough proxy — leaflets distributed by ward, outreach events attended, GP conversations logged — is better than nothing, because it lets you separate “nobody knew” from “people knew and declined.”

Step 4: Track drop-off by stage, and disaggregate it. If men are referred at the same rate as women but engage at half the rate, that is a finding, not noise. The 2021 American Enterprise Institute survey on friendship (cited elsewhere in this literature, though not the focus here) is a reminder that social withdrawal patterns differ sharply by group; a reach funnel that pools everyone together will hide that.

Step 5: Report the funnel alongside the outcome data, in the same document, to the same audience. Do not let reach data live in an internal operations file while outcome data goes to the funder. If the funder only sees outcomes, they will draw conclusions the reach data would contradict.

The named failure mode: the completer’s illusion

Call it the completer’s illusion: judging a service entirely by the experience of the people who finished it. It happens because completers are easy to survey — they’re still in contact, they answered the phone, they filled in the form. Everyone who dropped out earlier is, by definition, harder to reach for a satisfaction survey, which is exactly why they dropped out in the first place. The result is a systematic bias toward positive results that has nothing to do with whether the service works and everything to do with who stuck around long enough to be asked.

The UK’s 2018 loneliness strategy addressed this partly by embedding loneliness measurement into national statistics rather than leaving it to individual service reports — a structural fix that most local commissioners cannot replicate, but the principle transfers: measure the population, not just the caseload.

Evidence status

Claim	Evidence status
Social prescribing improves self-reported wellbeing among people who engage	Reasonably supported, but trial base is small and heterogeneous
Social prescribing reduces loneliness among engaged participants	Supported by systematic review, but all included studies measure engaged populations only
Isolated people are harder to reach through standard referral routes	Supported by risk-factor data, not by direct programme-reach studies
Reach funnels reduce inequity in who benefits from social prescribing	Plausible and consistent with the evidence above, but not directly tested in the published literature

What this does not solve

Tracking reach tells you who you are missing. It does not tell you how to reach them, and the honest answer is that most of the fixes — trusted community outreach, home visits, longer GP appointments, repeated non-judgemental contact — are expensive and slow, and no randomised evidence in this literature isolates their effect on reach specifically. Holt-Lunstad’s 2021 review makes the broader case for treating social connection as a preventive health factor worth investing in at population scale, but prevention framing does not by itself solve a staffing and outreach budget problem. A programme with better reach data will know its blind spots with more precision. It will not automatically have the resources to close them.